ŠŠ±Š½Š°ŃŃŠ¶ŠµŠ½ŠøŠµ ŃŠ¾Š±ŃŃŠøŠ¹, ŃŠ²ŃŠ·Š°Š½Š½ŃŃ Ń ŠŗŠ¾ŠæŃŠ¾Š¼ŠµŠ½ŃŠ°ŃŠøŠµŠ¹ ŠŗŠ¾ŃŠæŠ¾ŃŠ°ŃŠøŠ²Š½Š¾Š¹ ŃŠ»ŠµŠŗŃŃŠ¾Š½Š½Š¾Š¹ ŠæŠ¾ŃŃŃ, Š² ŠæŃŠ¾ŃŠµŃŃŠµ Š¼Š¾Š½ŠøŃŠ¾ŃŠøŠ½Š³Š° ŠøŃŃ Š¾Š“ŃŃŠµŠ¹ ŠŗŠ¾ŃŃŠµŃŠæŠ¾Š½Š“ŠµŠ½ŃŠøŠø Š½Š° Š¾ŃŠ½Š¾Š²Šµ Š¼ŠµŃŠ¾Š“Š¾Š² Š¼Š°ŃŠøŠ½Š½Š¾Š³Š¾ Š¾Š±ŃŃŠµŠ½ŠøŃĀ¶
ŠŠ¾Š“Š³Š¾ŃŠ¾Š²ŠŗŠ° Š“Š°ŃŠ°ŃŠµŃŠ¾Š²Ā¶
ŠŃŠæŠ¾Š»ŃŠ·ŃŠµŠ¼ Š¾ŃŠŗŃŃŃŃŠ¹ Š“Š°ŃŠ°ŃŠµŃ ŠŗŠ¾Š¼ŠæŠ°Š½ŠøŠø Enron. Š Š°Š·Š°ŃŃ ŠøŠ²ŠøŃŃŠµŠ¼ ŠµŠ³Š¾ Šø ŠæŃŠµŠ²ŃŠ°ŃŠøŠ¼ ŠµŠ³Š¾ Š² Š½ŃŠ¶Š½ŃŠµ Š½Š°Š¼ Š“Š°ŃŠ°ŃŠµŃŃ.
InĀ [1]:
!pip install -r requirements.txt
WARNING: The directory '/home/tech/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
Requirement already satisfied: pandas==2.2.1 in /home/tech/.local/lib/python3.9/site-packages (from -r requirements.txt (line 1)) (2.2.1) Requirement already satisfied: mail-parser==3.15.0 in /home/tech/.local/lib/python3.9/site-packages (from -r requirements.txt (line 2)) (3.15.0) Requirement already satisfied: scikit-learn==1.4.1.post1 in /home/tech/.local/lib/python3.9/site-packages (from -r requirements.txt (line 3)) (1.4.1.post1) Requirement already satisfied: numpy==1.26.4 in /home/tech/.local/lib/python3.9/site-packages (from -r requirements.txt (line 4)) (1.26.4) Requirement already satisfied: nltk==3.8.1 in /home/tech/.local/lib/python3.9/site-packages (from -r requirements.txt (line 5)) (3.8.1) Requirement already satisfied: tensorflow==2.16.1 in /home/tech/.local/lib/python3.9/site-packages (from -r requirements.txt (line 6)) (2.16.1) Requirement already satisfied: torch==2.2.1 in /home/tech/.local/lib/python3.9/site-packages (from -r requirements.txt (line 7)) (2.2.1) Requirement already satisfied: matplotlib==3.8.3 in /home/tech/.local/lib/python3.9/site-packages (from -r requirements.txt (line 8)) (3.8.3) Requirement already satisfied: seaborn==0.13.2 in /home/tech/.local/lib/python3.9/site-packages (from -r requirements.txt (line 9)) (0.13.2) Requirement already satisfied: tqdm==4.66.2 in /home/tech/.local/lib/python3.9/site-packages (from -r requirements.txt (line 10)) (4.66.2)
Collecting transformers==4.38.2
Downloading transformers-4.38.2-py3-none-any.whl (8.5 MB)
| | 10 kB 16.2 MB/s eta 0:00:01
| | 20 kB 2.6 MB/s eta 0:00:04
|ā | 30 kB 3.8 MB/s eta 0:00:03
|ā | 40 kB 1.3 MB/s eta 0:00:07
|ā | 51 kB 1.4 MB/s eta 0:00:07
|ā | 61 kB 1.7 MB/s eta 0:00:06
|ā | 71 kB 1.8 MB/s eta 0:00:05
|ā | 81 kB 1.9 MB/s eta 0:00:05
|ā | 92 kB 2.1 MB/s eta 0:00:05
|ā | 102 kB 1.6 MB/s eta 0:00:06
|ā | 112 kB 1.6 MB/s eta 0:00:06
|ā | 122 kB 1.6 MB/s eta 0:00:06
|ā | 133 kB 1.6 MB/s eta 0:00:06
|ā | 143 kB 1.6 MB/s eta 0:00:06
|ā | 153 kB 1.6 MB/s eta 0:00:06
|ā | 163 kB 1.6 MB/s eta 0:00:06
|ā | 174 kB 1.6 MB/s eta 0:00:06
|ā | 184 kB 1.6 MB/s eta 0:00:06
|ā | 194 kB 1.6 MB/s eta 0:00:06
|ā | 204 kB 1.6 MB/s eta 0:00:06
|ā | 215 kB 1.6 MB/s eta 0:00:06
|ā | 225 kB 1.6 MB/s eta 0:00:06
|ā | 235 kB 1.6 MB/s eta 0:00:06
|ā | 245 kB 1.6 MB/s eta 0:00:06
|ā | 256 kB 1.6 MB/s eta 0:00:06
|ā | 266 kB 1.6 MB/s eta 0:00:06
|ā | 276 kB 1.6 MB/s eta 0:00:06
|ā | 286 kB 1.6 MB/s eta 0:00:06
|āā | 296 kB 1.6 MB/s eta 0:00:06
|āā | 307 kB 1.6 MB/s eta 0:00:06
|āā | 317 kB 1.6 MB/s eta 0:00:06
|āā | 327 kB 1.6 MB/s eta 0:00:06
|āā | 337 kB 1.6 MB/s eta 0:00:06
|āā | 348 kB 1.6 MB/s eta 0:00:06
|āā | 358 kB 1.6 MB/s eta 0:00:06
|āā | 368 kB 1.6 MB/s eta 0:00:06
|āā | 378 kB 1.6 MB/s eta 0:00:06
|āā | 389 kB 1.6 MB/s eta 0:00:06
|āā | 399 kB 1.6 MB/s eta 0:00:06
|āā | 409 kB 1.6 MB/s eta 0:00:06
|āā | 419 kB 1.6 MB/s eta 0:00:06
|āā | 430 kB 1.6 MB/s eta 0:00:06
|āā | 440 kB 1.6 MB/s eta 0:00:06
|āā | 450 kB 1.6 MB/s eta 0:00:06
|āā | 460 kB 1.6 MB/s eta 0:00:06
|āā | 471 kB 1.6 MB/s eta 0:00:06
|āā | 481 kB 1.6 MB/s eta 0:00:06
|āā | 491 kB 1.6 MB/s eta 0:00:06
|āā | 501 kB 1.6 MB/s eta 0:00:06
|āā | 512 kB 1.6 MB/s eta 0:00:06
|āā | 522 kB 1.6 MB/s eta 0:00:06
|āā | 532 kB 1.6 MB/s eta 0:00:06
|āā | 542 kB 1.6 MB/s eta 0:00:06
|āā | 552 kB 1.6 MB/s eta 0:00:06
|āā | 563 kB 1.6 MB/s eta 0:00:06
|āāā | 573 kB 1.6 MB/s eta 0:00:06
|āāā | 583 kB 1.6 MB/s eta 0:00:06
|āāā | 593 kB 1.6 MB/s eta 0:00:06
|āāā | 604 kB 1.6 MB/s eta 0:00:06
|āāā | 614 kB 1.6 MB/s eta 0:00:06
|āāā | 624 kB 1.6 MB/s eta 0:00:06
|āāā | 634 kB 1.6 MB/s eta 0:00:06
|āāā | 645 kB 1.6 MB/s eta 0:00:06
|āāā | 655 kB 1.6 MB/s eta 0:00:06
|āāā | 665 kB 1.6 MB/s eta 0:00:06
|āāā | 675 kB 1.6 MB/s eta 0:00:06
|āāā | 686 kB 1.6 MB/s eta 0:00:06
|āāā | 696 kB 1.6 MB/s eta 0:00:06
|āāā | 706 kB 1.6 MB/s eta 0:00:06
|āāā | 716 kB 1.6 MB/s eta 0:00:05
|āāā | 727 kB 1.6 MB/s eta 0:00:05
|āāā | 737 kB 1.6 MB/s eta 0:00:05
|āāā | 747 kB 1.6 MB/s eta 0:00:05
|āāā | 757 kB 1.6 MB/s eta 0:00:05
|āāā | 768 kB 1.6 MB/s eta 0:00:05
|āāā | 778 kB 1.6 MB/s eta 0:00:05
|āāā | 788 kB 1.6 MB/s eta 0:00:05
|āāā | 798 kB 1.6 MB/s eta 0:00:05
|āāā | 808 kB 1.6 MB/s eta 0:00:05
|āāā | 819 kB 1.6 MB/s eta 0:00:05
|āāā | 829 kB 1.6 MB/s eta 0:00:05
|āāāā | 839 kB 1.6 MB/s eta 0:00:05
|āāāā | 849 kB 1.6 MB/s eta 0:00:05
|āāāā | 860 kB 1.6 MB/s eta 0:00:05
|āāāā | 870 kB 1.6 MB/s eta 0:00:05
|āāāā | 880 kB 1.6 MB/s eta 0:00:05
|āāāā | 890 kB 1.6 MB/s eta 0:00:05
|āāāā | 901 kB 1.6 MB/s eta 0:00:05
|āāāā | 911 kB 1.6 MB/s eta 0:00:05
|āāāā | 921 kB 1.6 MB/s eta 0:00:05
|āāāā | 931 kB 1.6 MB/s eta 0:00:05
|āāāā | 942 kB 1.6 MB/s eta 0:00:05
|āāāā | 952 kB 1.6 MB/s eta 0:00:05
|āāāā | 962 kB 1.6 MB/s eta 0:00:05
|āāāā | 972 kB 1.6 MB/s eta 0:00:05
|āāāā | 983 kB 1.6 MB/s eta 0:00:05
|āāāā | 993 kB 1.6 MB/s eta 0:00:05
|āāāā | 1.0 MB 1.6 MB/s eta 0:00:05
|āāāā | 1.0 MB 1.6 MB/s eta 0:00:05
|āāāā | 1.0 MB 1.6 MB/s eta 0:00:05
|āāāā | 1.0 MB 1.6 MB/s eta 0:00:05
|āāāā | 1.0 MB 1.6 MB/s eta 0:00:05
|āāāā | 1.1 MB 1.6 MB/s eta 0:00:05
|āāāā | 1.1 MB 1.6 MB/s eta 0:00:05
|āāāā | 1.1 MB 1.6 MB/s eta 0:00:05
|āāāā | 1.1 MB 1.6 MB/s eta 0:00:05
|āāāā | 1.1 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.1 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.1 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.1 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.1 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.1 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.2 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.2 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.2 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.2 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.2 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.2 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.2 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.2 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.2 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.2 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.3 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.3 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.3 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.3 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.3 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.3 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.3 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.3 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.3 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.4 MB 1.6 MB/s eta 0:00:05
|āāāāā | 1.4 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.4 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.4 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.4 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.4 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.4 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.4 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.4 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.4 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.5 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.5 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.5 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.5 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.5 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.5 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.5 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.5 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.5 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.5 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.6 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.6 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.6 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.6 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.6 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.6 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.6 MB 1.6 MB/s eta 0:00:05
|āāāāāā | 1.6 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.6 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.6 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.7 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.7 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.7 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.7 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.7 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.7 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.7 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.7 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.7 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.8 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.8 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.8 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.8 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.8 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.8 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.8 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.8 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.8 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.8 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.9 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.9 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.9 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.9 MB 1.6 MB/s eta 0:00:05
|āāāāāāā | 1.9 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 1.9 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 1.9 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 1.9 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 1.9 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 1.9 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.0 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.0 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.0 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.0 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.0 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.0 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.0 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.0 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.0 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.0 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.1 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.1 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.1 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.1 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.1 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.1 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.1 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.1 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.1 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.2 MB 1.6 MB/s eta 0:00:05
|āāāāāāāā | 2.2 MB 1.6 MB/s eta 0:00:05
|āāāāāāāāā | 2.2 MB 1.6 MB/s eta 0:00:05
|āāāāāāāāā | 2.2 MB 1.6 MB/s eta 0:00:05
|āāāāāāāāā | 2.2 MB 1.6 MB/s eta 0:00:05
|āāāāāāāāā | 2.2 MB 1.6 MB/s eta 0:00:05
|āāāāāāāāā | 2.2 MB 1.6 MB/s eta 0:00:05
|āāāāāāāāā | 2.2 MB 1.6 MB/s eta 0:00:05
|āāāāāāāāā | 2.2 MB 1.6 MB/s eta 0:00:05
|āāāāāāāāā | 2.2 MB 1.6 MB/s eta 0:00:05
|āāāāāāāāā | 2.3 MB 1.6 MB/s eta 0:00:05
|āāāāāāāāā | 2.3 MB 1.6 MB/s eta 0:00:05
|āāāāāāāāā | 2.3 MB 1.6 MB/s eta 0:00:05
|āāāāāāāāā | 2.3 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāā | 2.3 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāā | 2.3 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāā | 2.3 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāā | 2.3 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāā | 2.3 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāā | 2.3 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāā | 2.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāā | 2.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāā | 2.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāā | 2.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāā | 2.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāā | 2.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāā | 2.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāā | 2.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāā | 2.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.9 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.9 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.9 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.9 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.9 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.9 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.9 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.9 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.9 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 2.9 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāā | 3.0 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.0 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.0 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.0 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.0 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.0 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.0 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.0 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.0 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.1 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.1 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.1 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.1 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.1 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.1 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.1 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.1 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.1 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.1 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.2 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.2 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.2 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.2 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.2 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.2 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.2 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāā | 3.2 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.2 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.2 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.3 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.3 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.3 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.3 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.3 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.3 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.3 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.3 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.3 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.3 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.4 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāā | 3.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.5 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.6 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.7 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāā | 3.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāāā | 3.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāāā | 3.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāāā | 3.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāāā | 3.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāāā | 3.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāāā | 3.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāāā | 3.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāāā | 3.8 MB 1.6 MB/s eta 0:00:04
|āāāāāāāāāāāāāāā | 3.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāā | 3.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāā | 3.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāā | 3.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāā | 3.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāā | 3.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāā | 3.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāā | 3.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāā | 3.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāā | 3.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāā | 4.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāā | 4.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāā | 4.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāā | 4.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāā | 4.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāā | 4.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāā | 4.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāā | 4.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāā | 4.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāā | 4.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.4 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.4 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.4 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.4 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.4 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.4 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.4 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.4 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.4 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.4 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.5 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.5 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.5 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.5 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.5 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.5 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.5 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.5 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.5 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.5 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.6 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāā | 4.6 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.6 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.6 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.6 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.6 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.6 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.6 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.6 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.6 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.7 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.7 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.7 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.7 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.7 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.7 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.7 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.7 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.7 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.8 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.8 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.8 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.8 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.8 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.8 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.8 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.8 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāā | 4.8 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 4.8 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 4.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 4.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 4.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 4.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 4.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 4.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 4.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 4.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 4.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 4.9 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 5.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 5.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 5.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 5.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 5.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 5.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 5.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 5.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 5.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 5.0 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 5.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 5.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 5.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 5.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāā | 5.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.1 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.2 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.3 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.4 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāā | 5.4 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāāā | 5.4 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāāā | 5.4 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāāā | 5.4 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāāā | 5.4 MB 1.6 MB/s eta 0:00:03
|āāāāāāāāāāāāāāāāāāāāā | 5.4 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.4 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.4 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.4 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāā | 5.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāā | 5.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 5.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 5.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 5.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 5.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 5.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.0 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.0 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.0 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.0 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.0 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.0 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.0 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.0 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.0 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.1 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.1 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.1 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.1 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.1 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.1 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.1 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.1 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.1 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.1 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.2 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāā | 6.2 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.2 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.2 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.2 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.2 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.2 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.2 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.2 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.2 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.3 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.3 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.3 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.3 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.3 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.3 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.3 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.3 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.3 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.3 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.4 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.4 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.4 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.4 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.4 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.4 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.4 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāā | 6.4 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.4 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.5 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.6 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāā | 6.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.7 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.8 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 6.9 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 7.0 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāā | 7.0 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.0 MB 1.6 MB/s eta 0:00:02
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.0 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.0 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.0 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.0 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.0 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.0 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.0 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.6 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.6 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.6 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.6 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.6 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.6 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.6 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.6 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.6 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.6 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.7 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.7 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.7 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.7 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.7 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.7 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.7 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.7 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.7 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.8 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.8 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.8 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.8 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.8 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.8 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.8 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.8 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.8 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.8 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.9 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.9 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.9 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.9 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.9 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.9 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.9 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.9 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.9 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 7.9 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.0 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.0 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.0 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.0 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.0 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.0 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.0 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.0 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.0 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.0 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.1 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.2 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā | 8.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.3 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.4 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.5 MB 1.6 MB/s eta 0:00:01
|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 8.5 MB 1.6 MB/s
ERROR: Could not find a version that satisfies the requirement ipython==8.22.2 ERROR: No matching distribution found for ipython==8.22.2
InĀ [2]:
import os
import mailparser
import re
import pandas as pd
ŠŠ°Šŗ Š²ŃŠ³Š»ŃŠ“ŠøŃ ŃŠøŠæŠøŃŠ½Š¾Šµ ŠæŠøŃŃŠ¼Š¾ ŠøŠ· Š“Š°Š½Š½ŃŃ .
InĀ [3]:
with open("enron_mail_20150507/maildir/delainey-d/sent/1.") as f:
print(f.read())
Message-ID: <4591008.1075854462117.JavaMail.evans@thyme> Date: Tue, 12 Dec 2000 12:19:00 -0800 (PST) From: david.delainey@enron.com To: janet.dietrich@enron.com, christopher.calger@enron.com, brian.redmond@enron.com, max.yzaguirre@enron.com, rob.milnthorp@enron.com Subject: Re: MEH Turbines Mime-Version: 1.0 Content-Type: text/plain; charset=us-ascii Content-Transfer-Encoding: 7bit X-From: David W Delainey X-To: Janet R Dietrich, Christopher F Calger, Brian Redmond, Max Yzaguirre, Rob Milnthorp X-cc: X-bcc: X-Folder: \David_Delainey_Dec2000\Notes Folders\Sent X-Origin: Delainey-D X-FileName: ddelain.nsf Guys, here are the details on the ESA MEH turbines - please forward to me your project details, economics and strategy ASAP. We will allocate these turbines to the project with the best strategic/economic rationale once confirming that they are worth more in ENA versus ESA. If you need more detailed information on these turbines please let me know. Rob, please give me call on this one. Regards Delainey ---------------------- Forwarded by David W Delainey/HOU/ECT on 12/12/2000 08:17 PM --------------------------- Brett R Wiggs@ENRON 12/11/2000 11:54 AM To: David W Delainey/HOU/ECT@ECT cc: Joe Kishkill/SA/Enron@Enron Subject: Re: MEH Turbines Dave: The attached sheet provides a summary of information on the turbines. Brett David W Delainey@ECT 12/08/2000 06:38 PM To: Brett R Wiggs/SA/Enron@Enron cc: Joe Kishkill/SA/Enron@Enron Subject: MEH Turbines Brett, can you give me the particulars on the four MEH turbines (ie) scheduled delivery, price, fuel options, heat rate, output,etc - including transferablility to NA. Regards Delainey
ŠŠøŃŃŠ¼Š° ŃŠ°ŃŠŗŠøŠ“Š°Š½Ń ŠæŠ¾ ŠæŠ°ŠæŠŗŠ°Š¼. ŠŠµŃŃŠ¼ Š²ŃŠµ ŠæŠ°ŠæŠŗŠø, Š² ŠŗŠ¾ŃŠ¾ŃŃŃ Š²ŃŃŃŠµŃŠ°ŠµŃŃŃ ŃŠ»Š¾Š²Š¾ sent - ŠæŠ°ŠæŠŗŠø Ń ŠøŃŃ Š¾Š“ŃŃŠµŠ¹ ŠŗŠ¾ŃŃŠµŃŠæŠ¾Š½Š“ŠµŠ½ŃŠøŠµŠ¹.
InĀ [4]:
print(os.listdir("enron_mail_20150507/maildir/delainey-d"))
['all_documents', 'notes_inbox', 'inbox', 'discussion_threads', 'sent', 'sent_items', 'deleted_items', '_sent_mail']
InĀ [5]:
def parse_emails_by_sender(path="enron_mail_20150507"):
outgoing_emails_by_user = {}
unsuccessful = 0
if not os.path.exists(path):
return
total_emails = 0
for root, dirs, files in os.walk(path):
# ŠŠ±ŃŠ°Š±Š°ŃŃŠ²Š°ŠµŠ¼ ŃŠ¾Š»ŃŠŗŠ¾ Š¾ŃŠæŃŠ°Š²Š»ŠµŠ½Š½ŃŠµ ŃŠ¾Š¾Š±ŃŠµŠ½ŠøŃ
if not ("sent" in root):
continue
for file_name in files:
try:
msg = mailparser.parse_from_file(os.path.join(root, file_name))
sender = msg.headers.get("From")
body = msg.body
if sender:
sender = re.search(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', sender)
if sender:
sender = sender.group().lower()
if sender not in outgoing_emails_by_user:
outgoing_emails_by_user[sender] = set()
outgoing_emails_by_user[sender].add(body)
total_emails += 1
except Exception:
unsuccessful += 1
print(f"Total unsuccessful: {unsuccessful}/{total_emails}")
return outgoing_emails_by_user
def extract_reply_from_email(text: str, sender_email: str):
i = text.find("\n\n\n")
if i != -1:
text = text[:i]
i = text.find("----- Forwarded")
if i != -1:
text = text[:i]
i = text.find("Original Message")
if i != -1:
text = text[:i]
text = re.sub("-{3,50}", "", text)
name = sender_email.split("@")[0].split(".")
if name:
if len(name) > 1:
r = re.compile(re.escape(name[1]), re.IGNORECASE)
text = r.sub("", text)
if len(name[0]) > 2:
r = re.compile(re.escape(name[0]), re.IGNORECASE)
text = r.sub("", text)
text = text.rstrip(" \n")
text = re.sub(r'(\n\n.{3,50})$', '', text)
text = re.sub("thanks.?", "", text, flags=re.IGNORECASE)
text = re.sub(r"(\w{3,10}[,:](\n|\n\n)|^\w{3,10}[,:]|\n\w{3,10}[,:])", "", text)
return text
InĀ [6]:
emails_dict = parse_emails_by_sender(path="enron_mail_20150507")
skip_emails = {"no.address@enron.com", "40enron@enron.com"}
senders, emails = [], []
for sender, emails_ in zip(emails_dict.keys(), emails_dict.values()):
if sender not in skip_emails:
unique_emails = set()
for email in emails_:
email = extract_reply_from_email(email, sender)
if len(email) > 10 and "Outlook Migration Team" not in email:
unique_emails.add(email)
for email in unique_emails:
senders.append(sender)
emails.append(email)
df = pd.DataFrame(list(zip(senders, emails)), columns=['sender', 'text'])
df.to_csv("raw_emails.csv")
Total unsuccessful: 5/126841
InĀ [7]:
import random
InĀ [8]:
df = pd.read_csv('raw_emails.csv')
# print(df["sender"].value_counts()) # ŠŃŠ“Šø Ń Š¼Š°ŠŗŃŠøŠ¼Š°Š»ŃŠ½ŃŠ¼ Šø Š¼ŠøŠ½ŠøŠ¼Š°Š»ŃŠ½ŃŠ¼ ŃŠøŃŠ»Š¾Š¼ ŠæŠøŃŠµŠ¼
d = df.to_dict()
senders = list(d["sender"].values())
texts = list(d["text"].values())
datasets = dict()
for i, sender in enumerate(senders):
if sender in datasets:
datasets[sender].append([texts[i], 1])
else:
datasets[sender] = [[texts[i], 1]]
numbers_of_owned_emails = {sender: len(datasets[sender]) for sender in datasets}
for sender in datasets:
other_texts = []
for i, text in enumerate(texts):
if sender != senders[i]:
other_texts.append(text)
for _ in range(numbers_of_owned_emails[sender]):
datasets[sender].append([random.choice(other_texts), 0])
if not os.path.exists("datasets"):
os.mkdir("datasets")
for sender in datasets:
df = pd.DataFrame(list(zip([row[0] for row in datasets[sender]],
[row[1] for row in datasets[sender]])),
columns=['text', 'label'])
df.to_csv(os.path.join("datasets", f"{sender.split('@')[0].replace('.', '_')}.csv"))
Š ŠøŃŠ¾Š³Šµ, ŠæŠ¾Š»ŃŃŠ°ŠµŠ¼ ŠæŠ°ŠæŠŗŃ datasets Ń Š“Š°ŃŠ°ŃŠµŃŠ¾Š¼ Š“Š»Ń ŠŗŠ°Š¶Š“Š¾Š³Š¾ ŃŠ°Š±Š¾ŃŠ½ŠøŠŗŠ° ŠŗŠ¾Š¼ŠæŠ°Š½ŠøŠø, Š² ŠŗŠ¾ŃŠ¾ŃŠ¾Š¼ ŃŠ°Š·Š¼ŠµŃŠµŠ½Ń ŠæŠøŃŃŠ¼Š° (ŠæŠøŃŃŠ¼Š° Š¾Ń ŃŃŠ¾Š³Š¾ ŃŠµŠ»Š¾Š²ŠµŠŗŠ° - 1, Š“ŃŃŠ³ŠøŠµ ŠæŠøŃŃŠ¼Š° - 0). ŠŃŃŠ³ŠøŠµ ŠæŠøŃŃŠ¼Š° Š²ŃŠ±ŠøŃŠ°ŃŃŃŃ ŃŠ»ŃŃŠ°Š¹Š½ŃŠ¼ Š¾Š±ŃŠ°Š·Š¾Š¼.
InĀ [9]:
print(os.listdir("datasets"))
['shelley_corman.csv', 'john_buchanan.csv', 'lynn_blair.csv', 'bradley_holmes.csv', 'toby_kuehl.csv', 'maggie_matheson.csv', 'robert_badeer.csv', 'richard_ring.csv', 'martin_cuilla.csv', 'brad_mckay.csv', 'diana_scholtes.csv', 'greg_whalley.csv', 'liz_taylor.csv', 'michelle_cash.csv', 'twanda_sweet.csv', 'carol_coats.csv', 'robert_benson.csv', 'mary_hain.csv', 'lysa_akin.csv', 'mark_guzman.csv', 'larry_may.csv', 'kam_keiser.csv', 'm__smith.csv', 'rosalee_fleming.csv', 'tori_wells.csv', 'kenneth_lay.csv', 'scott_neal.csv', 'kimberly_brown.csv', 'pam_butler.csv', 'marie_heard.csv', 'tamara_black.csv', 'kevin_presto.csv', 'm__presto.csv', 'david_forster.csv', 'rogers_herndon.csv', 'louise_kitchen.csv', 'debra_bailey.csv', 'madhup_kumar.csv', 'dutch_quigley.csv', 'monika_causholli.csv', 'laura_vuittonet.csv', 'a__martin.csv', 'thomas_martin.csv', 'bill_williams.csv', 'stacy_dickson.csv', 'matthew_lenhart.csv', 'robin_rodrigue.csv', 'stephanie_panus.csv', 'mark_taylor.csv', 'taylor.csv', 'e_taylor.csv', 'albert_meyers.csv', 'geir_solberg.csv', 'david_delainey.csv', 'kay_chapman.csv', 'beverly_stephens.csv', 'w__delainey.csv', 'jeff_dasovich.csv', 'joseph_alamo.csv', 'susan_bailey.csv', 'holden_salisbury.csv', 'darrell_schoolcraft.csv', 'jane_joyce.csv', 'michael_norden.csv', 'danny_mccarty.csv', 'drew_fossum.csv', 'martha_benner.csv', 'derek_r_ahmann.csv', 'benjamin_rogers.csv', 'clint_dean.csv', 'craig_dean.csv', 'm__forney.csv', 'john_forney.csv', 'tom_donohoe.csv', 'peter_keavey.csv', 'f__keavey.csv', 'eric_linder.csv', 'kevin_ruscitti.csv', 'john_griffith.csv', 'eric_saibi.csv', 'joe_stepenovitch.csv', 'sean_crandall.csv', 'jason_wolfe.csv', 'hunter_shively.csv', 's__shively.csv', 'jim_schwieger.csv', 'doug_gilbert-smith.csv', 'barry_tycholiz.csv', 'eric_moon.csv', 'chris_germany.csv', 'dkinney.csv', 'steven_merris.csv', 'gerald_nemec.csv', 'sherri_sera.csv', 'joannie_williamson.csv', 'sherri_reinartz.csv', 'katherine_brown.csv', 'jeff_skilling.csv', 'amelia_alder.csv', 'enron_announcements.csv', 'd__steffes.csv', 'elizabeth_sager.csv', 'mike_grigsby.csv', 'debra_perlingiere.csv', 'frank_ermis.csv', 'reminger.csv', 'ursula_brenner.csv', 'llaidlaw.csv', 'lora_sullivan.csv', 'katrina_burton.csv', 'terry_west.csv', 'andrew_miles.csv', 'rita_hartfield.csv', 'pges.csv', 'margaret_carson.csv', 'lexi_elliott.csv', 'linda_robertson.csv', 'elizabeth_linnell.csv', 'michael_grimes.csv', 'rob_bradley.csv', 'mark_schroeder.csv', 'richard_shapiro.csv', 'mark_palmer.csv', 'maureen_raymond.csv', 'maureen_mcvicker.csv', 'sgaines1.csv', 'simon_shih.csv', 'ginger_dernehl.csv', 'alhamd_alkhayat.csv', 'miyung_buster.csv', 'lysa_tracy.csv', 'james_steffes.csv', 'gwyn_koepke.csv', 'janet_butler.csv', 'jeff_gray.csv', 'sergai_daigre.csv', 'jeffrey_keeler.csv', 'steven_kean.csv', 'j__kean.csv', 'john_lavorato.csv', 'kimberly_hillis.csv', 'angela_mcculloch.csv', 'lavorato.csv', 'darin_talley.csv', 'jane_tholt.csv', 'm__tholt.csv', 'errol_mclaughlin.csv', 'steven_south.csv', 'barbo.csv', 'dan_hyvl.csv', 'andrew_lewis.csv', 'h__lewis.csv', 'mike_maggi.csv', 'andy_zipper.csv', 'joe_quenet.csv', 'vladi_pimenov.csv', 'eric_bass.csv', 'randall_gay.csv', 'rob_gay.csv', 'l__gay.csv', 'phillip_love.csv', 'm__love.csv', 'tana_jones.csv', 'sandra_brawner.csv', 'f__brawner.csv', 'mark_fisher.csv', 'mary_fischer.csv', 'mark_haedicke.csv', 'janette_elbertson.csv', 'sylvia_sauseda.csv', 'carol_kincannon.csv', 'e__haedicke.csv', 'mark_e_haedicke.csv', 'matt_smith.csv', 'bill_rapp.csv', 'teb_lokey.csv', 'sheila_nacey.csv', 'gaby_adamek.csv', 'd__martin.csv', 'judy_hernandez.csv', 'juan_hernandez.csv', 'phillip_platter.csv', 'sevil_yaman.csv', 'grant_masson.csv', 'vasant_shanbhogue.csv', 'stinson_gibner.csv', 'anita_dupont.csv', 'shirley_crenshaw.csv', 'vince_kaminski.csv', 'pinnamaneni_krishnarao.csv', 'vkaminski.csv', 'alla_gil.csv', 'rob_wilson.csv', 'j_kaminski.csv', 'kaminski.csv', 'j__kaminski.csv', 'cooper_richey.csv', 'fletcher_sturm.csv', 'j__sturm.csv', 'casey_evans.csv', 'jeff_king.csv', 'jeffrey_hodge.csv', 'jenny_helton.csv', 't__hodge.csv', 'john_hodge.csv', 'suzanne_adams.csv', 'cheryl_johnson.csv', 'jay_reitmeyer.csv', 'daren_farmer.csv', 'j__farmer.csv', 'd__thomas.csv', 'paul_thomas.csv', 'susan_pereira.csv', 'w__pereira.csv', 'click_home.csv', 'rick_buy.csv', 'karen_heathman.csv', 'kate_symes.csv', 'rod_hayslett.csv', 'a__howard.csv', 'tracy_geaccone.csv', 'scott_hendrickson.csv', 'susan_scott.csv', 'm__scott.csv', 'darron_giron.csv', 'c__giron.csv', 'elena_chilkina.csv', 'phillip_allen.csv', 'ina_rangel.csv', 'k__allen.csv', 'kevin_hyatt.csv', 'ryan_slinger.csv', 'sara_shackleton.csv', 'kaye_ellis.csv', 'theresa_staab.csv', 'lindy_donoho.csv', 'sally_beck.csv', 'patti_thompson.csv', 'gretel_smith.csv', 'cara_semperger.csv', 'skennedy.csv', 'suresh_raghavan.csv', 'harry_arora.csv', 'judy_townsend.csv', 'richard_sanders.csv', 'b__sanders.csv', 'lisa_gang.csv', 'jeffrey_shankman.csv', 'a__shankman.csv', 'john_zufferli.csv', 'mark_whitt.csv', 'james_derrick.csv', 'j_harris.csv', 'zionette_vincent.csv', 'stacey_white.csv', 'w__white.csv', 'c__gossett.csv', 'scotty_gilbert.csv', 'greg_couch.csv', 'sonia_hennessy.csv', 'torrey_moorer.csv', 'terry_sanvido.csv', 'carl_tricoli.csv', 'stanley_horton.csv', 'cindy_stark.csv', 'horton.csv', 'don_baughman.csv', 'joe_parks.csv', 'keith_holst.csv', 'mike_carson.csv', 'kay_mann.csv', 'patrice_mims.csv', 'l__mims.csv', 'matt_motley.csv', 'dana_davis.csv', 'carol_clair.csv', 't__lucci.csv', 'mike_swerzbin.csv', 'andrea_ring.csv', 'john_arnold.csv', 'melissa_dozier.csv', 'monique_sanchez.csv', 'geoff_storey.csv', 'chris_dorland.csv', 'liz_prior.csv', 'kim_ward.csv', 'ward.csv', 's__ward.csv', 'brenda_whitehead.csv', 'michelle_lokay.csv', 'michele_lokay.csv', 'v_weldon.csv', 'charles_weldon.csv', 'chris_stokley.csv', 'daniel_kang.csv', 'paige_grumulaitis.csv', 'kimberly_watson.csv', 'jonathan_mckay.csv', 'peggy_hedstrom.csv', 'jason_williams.csv', 'williams.csv', 'mark_koenig.csv', 'julie_ferrara.csv', 'jason_rios.csv', 'cathy_phillips.csv', 'jonathon_pielop.csv', 'rick_bergsieker.csv', 'brenda_johnston.csv', 'mike_mcconnell.csv', 'mark_mcconnell.csv', 'tori_kuykendall.csv', 'larry_campbell.csv', 'f__campbell.csv', 'rjbaker.csv']
Š”Š°Š¼ŃŠµ ŃŠ°ŃŠæŃŠ¾ŃŃŃŠ°Š½ŠµŠ½Š½ŃŠµ ŃŠ»Š¾Š²Š° Š² ŃŠ¾Š¾Š±ŃŠµŠ½ŠøŃŃ .
InĀ [10]:
from collections import Counter
words_stat = dict()
for dataset in os.listdir("datasets"):
df = pd.read_csv(f"datasets/{dataset}")
for text in df["text"].values:
for pair in Counter(text.replace('\n', '').split()).most_common(100):
if pair[0] in words_stat:
words_stat[pair[0]] += pair[1]
else:
words_stat[pair[0]] = pair[1]
print(sorted(words_stat.items(), key=lambda x:x[1], reverse=True)[:100])
[('the', 269281), ('to', 217786), ('I', 133642), ('and', 124292), ('a', 107152), ('of', 97071), ('you', 94981), ('for', 81231), ('is', 78149), ('in', 76594), ('that', 68622), ('on', 60117), ('have', 55830), ('be', 53226), ('with', 50363), ('this', 46610), ('we', 45029), ('will', 42772), ('are', 37346), ('me', 36049), ('it', 35508), ('at', 31987), ('not', 26700), ('your', 26269), ('as', 25740), ('if', 25400), ('my', 23742), ('from', 23649), ('would', 23612), ('was', 22339), ('or', 21346), ('know', 21263), ('get', 20389), ('can', 20177), ('but', 19325), ('-', 19316), ('The', 17538), ('about', 16808), ('our', 16387), ('need', 15880), ('any', 15765), ('an', 15721), ('am', 15319), ('do', 15077), ('so', 15046), ('by', 14782), ('has', 14777), ('out', 14710), ('Please', 14612), ('all', 14267), ('up', 14098), ('like', 13633), ('We', 13493), ('what', 12546), ('should', 12458), ('they', 12402), ('think', 12336), ('just', 12076), ('Enron', 11652), ('call', 11560), ("I'm", 11431), ('i', 11287), ('going', 11234), ('some', 11139), ('he', 10759), ('been', 10731), ('let', 10647), ('If', 9650), ('please', 9471), ('there', 9269), ("don't", 9223), ('had', 9097), ('want', 8963), ('one', 8832), ('when', 8823), ('see', 8803), ('time', 8784), ('make', 7980), ('which', 7879), ('back', 7657), ('them', 7558), ('more', 7354), ('these', 7274), ('send', 7197), ('were', 7165), ("I'll", 7147), ('could', 7136), ('good', 7052), ('go', 7020), ('This', 7020), ('may', 7000), ('new', 6953), ('other', 6702), ('last', 6593), ('It', 6568), ('deal', 6366), ('next', 6351), ('their', 6269), ('work', 6258), ('how', 6137)]
ŠŃŠ±Š¾Ń Š“Š°ŃŠ°ŃŠµŃŠ¾Š² Š“Š»Ń Š¾Š±ŃŃŠµŠ½ŠøŃĀ¶
ŠŃŠ±ŠµŃŠµŠ¼ 5 ŃŠµŠ»Š¾Š²ŠµŠŗ, Ń ŠŗŠ¾ŃŠ¾ŃŃŃ Š±Š¾Š»ŃŃŠµ Š²ŃŠµŠ³Š¾ ŠøŃŃ Š¾Š“ŃŃŠøŃ ŠæŠøŃŠµŠ¼ Šø Š¾ŃŃŃŃŃŃŠ²ŃŃŃ ŠæŠ¾Š“ŠæŠøŃŠø Šŗ ŠæŠøŃŃŠ¼Š°Š¼.
InĀ [11]:
chosen_datasets = ['kate_symes.csv', 'sally_beck.csv', 'carol_clair.csv', 'michelle_cash.csv', 'chris_germany.csv']
InĀ [12]:
for dataset in chosen_datasets:
print(pd.read_csv(f"datasets/{dataset}"))
Unnamed: 0 text label
0 0 I'm a little confused - 559066 is a Prebon dea... 1
1 1 Prebon is right on this. Both deals are 25 MW.... 1
2 2 Hey there stranger!\n\nfor the pictures - they... 1
3 3 Two of these should have fees; two should not.... 1
4 4 Mark's changing deal 581615 to APB - he had it... 1
... ... ... ...
2529 2529 pending the sale of the Wilton Centre unit, I... 0
2530 2530 Same to you! \nAnd I hope you and your family... 0
2531 2531 \n I would appreciate your help in locating fi... 0
2532 2532 \t2- SURVEY/INFORMATION EMAIL - 7/19/01\n\nCur... 0
2533 2533 see if this works! If it does, see "13." w... 0
[2534 rows x 3 columns]
Unnamed: 0 text label
0 0 I won't sent this to you daily, but this is wh... 1
1 1 \n\nWhen I tried to approve this request (She... 1
2 2 As discussed - happy reading on the plane...\n... 1
3 3 I will be in London the week of January 15th. ... 1
4 4 I have completed internal feedback forms throu... 1
... ... ... ...
2815 2815 glad you were succesful, i am not, getting run... 0
2816 2816 I believe our new Tennessee rep is Sherry Glaz... 0
2817 2817 \n\nI found a copy of the confirmation letter... 0
2818 2818 Deal NN6788.1 with Citibank has been killed. ... 0
2819 2819 It's really easy to assume that companies are ... 0
[2820 rows x 3 columns]
Unnamed: 0 text label
0 0 This is the e-mail where DF objected to my pro... 1
1 1 Attached is a draft of 2 minor amendments that... 1
2 2 Sara and I would like to meet with you sometim... 1
3 3 suzanne;\nchocolate works!\n\n St. \nEB 3892\n... 1
4 4 There are a number of factors that determine p... 1
... ... ... ...
2229 2229 I still need the info I requested to comment i... 0
2230 2230 Absolutely. 0
2231 2231 for your help. Due to my lack of a complete u... 0
2232 2232 Brent Price, Bob Hall, Leslie Reeves and I wou... 0
2233 2233 You should try looking at Duke, they seem to ... 0
[2234 rows x 3 columns]
Unnamed: 0 text label
0 0 \nI will defer to your expertise on whether we... 1
1 1 \nso much for extending your business trip to ... 1
2 2 we could set up an LLC entity to employ those... 1
3 3 I just called our counsel on this matter this... 1
4 4 I am working with Mary Joyce on that right now... 1
... ... ... ...
2021 2021 I have lunch plans tomorrow (and I'm not sorry... 0
2022 2022 micha will pick us up at my place saturday @ 1... 0
2023 2023 try having no work to do for two months and th... 0
2024 2024 i think i am going to go to the game. 0
2025 2025 I was not aware that the market closed at 1:00... 0
[2026 rows x 3 columns]
Unnamed: 0 text label
0 0 Kathryn (EES Logistics) is going to send me s... 1
1 1 This doesn't have everything on it AND i thoug... 1
2 2 I faxed a copy of the PSNC invoice for March t... 1
3 3 not a bad idea.\n\n_61@hotmail.com\t\n2701 Re... 1
4 4 Should I call you up and sing !?!?!!?!?!?!?! ... 1
... ... ... ...
5929 5929 For your information. please find attached the... 0
5930 5930 Sorry. Don't work. I can do 1:45 California ... 0
5931 5931 \nDo you know anything about the Congress Plaz... 0
5932 5932 Please email me whatever version of the doc yo... 0
5933 5933 I have no such studies. 0
[5934 rows x 3 columns]
ŠŠ±ŃŃŠµŠ½ŠøŠµ Š¼Š¾Š“ŠµŠ»ŠµŠ¹Ā¶
InĀ [13]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pickle
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, precision_score, recall_score, \
precision_recall_curve, ConfusionMatrixDisplay
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
ŠŃŠ“ŠµŠ¼ ŠøŃŠæŠ¾Š»ŃŠ·Š¾Š²Š°ŃŃ 2 ŃŠæŠ¾ŃŠ¾Š±Š° Š²ŠµŠŗŃŠ¾ŃŠøŠ·Š°ŃŠøŠø ŃŠµŠŗŃŃŠ¾Š² - TF-IDF Šø CountVectorizer
InĀ [14]:
def tfidf(df: pd.DataFrame):
tfidf = TfidfVectorizer()
return np.array(tfidf.fit_transform(df['text']).todense())
def vectorize(df: pd.DataFrame):
count_vec = CountVectorizer()
return np.array(count_vec.fit_transform(df['text']).todense())
ŠŠæŃŠµŠ“ŠµŠ»ŃŠµŠ¼ ŠŗŠ»Š°ŃŃ Š“Š»Ń Š¾Š±ŃŃŠµŠ½ŠøŃ Šø ŃŠµŃŃŠøŃŠ¾Š²Š°Š½ŠøŃ Š¼Š¾Š“ŠµŠ»ŠµŠ¹. ŠŠ±ŃŃŠ°ŃŃŠ°Ń Š²ŃŠ±Š¾ŃŠŗŠ° - 70% Š“Š°ŃŠ°ŃŠµŃŠ°. to_vec_func - Š²ŃŠ±ŃŠ°Š½Š½ŃŠ¹ ŃŠæŠ¾ŃŠ¾Š± Š²ŠµŠŗŃŠ¾ŃŠøŠ·Š°ŃŠøŠø.
InĀ [15]:
class ModelTrainer:
def __init__(self, csv_path, train_dir_path):
self.dataset = pd.read_csv(os.path.join("datasets", csv_path))
self.dataset_name = csv_path.split(".")[0]
self.train_info = {}
self.train_dir_path = train_dir_path
def update_train_info(self, info, model_name, vec_name):
if model_name in self.train_info:
self.train_info[model_name][vec_name] = info
else:
self.train_info[model_name] = {vec_name: info}
def save_train_info_into_pickle(self):
if not os.path.exists(self.train_dir_path):
os.mkdir(self.train_dir_path)
with open(os.path.join(self.train_dir_path, f"{self.dataset_name}.pickle"), "wb") as f:
pickle.dump(self.train_info, f)
def count_acc(self, pred_scores, scores):
correct = 0
res_acc = []
pred_scores = list(pred_scores)
scores = list(scores)
for i in range(len(scores)):
if pred_scores[i] == scores[i]:
correct += 1
res_acc.append(correct / (i + 1))
return res_acc
def plot_accuracy(self, train_pred_scores, train_scores, test_pred_scores, test_scores):
plt.figure(figsize=(15, 8))
plt.title('Accuracy')
plt.xticks([])
plt.plot(self.count_acc(train_pred_scores, train_scores), color='r', label="Train")
plt.plot(self.count_acc(test_pred_scores, test_scores), color='b', label="Test")
plt.legend()
plt.show()
def plot_precision_recall_curve(self, y_test, y_pred):
precision, recall, thresholds = precision_recall_curve(y_test, y_pred)
plt.figure(figsize=(15, 8))
plt.xlabel('Recall')
plt.ylabel('Presicion')
plt.title('Precision-recall curve')
plt.xticks(np.arange(0, 1.1, 0.1))
plt.yticks(np.arange(0, 1.1, 0.1))
plt.grid()
plt.plot(recall, precision)
plt.show()
def plot_confusion_matrix(self, conf_matrix):
cm = ConfusionMatrixDisplay(conf_matrix)
cm.plot()
plt.show()
def train(self, model, to_vec_func):
model_name = model.__class__.__name__
X = to_vec_func(self.dataset)
y = self.dataset["label"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
model = model.fit(X_train, y_train)
y_train_pred = model.predict(X_train)
y_pred = model.predict(X_test)
self.plot_accuracy(y_train_pred, y_train, y_pred, y_test)
self.plot_precision_recall_curve(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)
self.plot_confusion_matrix(conf_matrix)
self.update_train_info({
'Accuracy': accuracy_score(y_test, y_pred),
'Precision': precision_score(y_test, y_pred),
'Recall': recall_score(y_test, y_pred),
'F1 score': f1_score(y_test, y_pred, average="macro"),
'Confusion matrix': conf_matrix
}, model_name, to_vec_func.__name__)
InĀ [16]:
models = [MultinomialNB, LogisticRegression, SVC, KNeighborsClassifier, DecisionTreeClassifier, RandomForestClassifier, GradientBoostingClassifier]
for dataset in chosen_datasets:
model_trainer = ModelTrainer(dataset, train_dir_path="train")
print(f"Start processing {dataset}")
for i, model in enumerate(models):
model_trainer.train(model(), vectorize)
model_trainer.train(model(), tfidf)
print(f"Processed model {i + 1}/{len(models)}")
model_trainer.save_train_info_into_pickle()
Start processing kate_symes.csv
Processed model 1/7
Processed model 2/7
Processed model 3/7
Processed model 4/7
Processed model 5/7
Processed model 6/7
Processed model 7/7 Start processing sally_beck.csv
Processed model 1/7
Processed model 2/7
Processed model 3/7
Processed model 4/7
Processed model 5/7
Processed model 6/7
Processed model 7/7 Start processing carol_clair.csv
Processed model 1/7
Processed model 2/7
Processed model 3/7
Processed model 4/7
Processed model 5/7
Processed model 6/7
Processed model 7/7 Start processing michelle_cash.csv
Processed model 1/7
Processed model 2/7
Processed model 3/7
Processed model 4/7
Processed model 5/7
Processed model 6/7
Processed model 7/7 Start processing chris_germany.csv
Processed model 1/7
Processed model 2/7
Processed model 3/7
Processed model 4/7
Processed model 5/7
Processed model 6/7
Processed model 7/7
InĀ [17]:
import copy
InĀ [18]:
def analyze_train_data(dir_path):
res_data = {}
for train_data in os.listdir(dir_path):
with open(os.path.join(dir_path, train_data), 'rb') as f:
d = pickle.load(f)
for name in d:
if name not in res_data:
res_data[name] = {'vectorize': {}, 'tfidf': {}}
for vec_name in d[name]:
for key in d[name][vec_name]:
res_data[name][vec_name][key] = [d[name][vec_name][key]]
else:
for vec_name in d[name]:
for key in d[name][vec_name]:
res_data[name][vec_name][key].append(d[name][vec_name][key])
tmp = copy.deepcopy(res_data)
max_score = 0
model_name = ""
for m in tmp:
for vec_name in tmp[m]:
for key in tmp[m][vec_name]:
if key != "Confusion matrix":
res_data[m][vec_name][key] = np.average(res_data[m][vec_name][key])
if key == "Accuracy" and res_data[m][vec_name][key] > max_score:
max_score = res_data[m][vec_name][key]
model_name = m
else:
res_data[m][vec_name].pop("Confusion matrix")
print(res_data)
print(f"Best accuracy: {max_score}")
print(f"Best model: {model_name}")
analyze_train_data("train")
{'MultinomialNB': {'vectorize': {'Accuracy': 0.8565354415032548, 'Precision': 0.8202021862163796, 'Recall': 0.9179097840607058, 'F1 score': 0.8555472784041329}, 'tfidf': {'Accuracy': 0.8088400276837258, 'Precision': 0.7571478099465017, 'Recall': 0.9313438958634365, 'F1 score': 0.8038208351905329}}, 'LogisticRegression': {'vectorize': {'Accuracy': 0.8493985384405761, 'Precision': 0.8700038378656683, 'Recall': 0.8250039639547498, 'F1 score': 0.8492631601967003}, 'tfidf': {'Accuracy': 0.8632770075957268, 'Precision': 0.8997734503253921, 'Recall': 0.8196084464144817, 'F1 score': 0.8629643720071687}}, 'SVC': {'vectorize': {'Accuracy': 0.8042504007652574, 'Precision': 0.8771756820210038, 'Recall': 0.7090956022300853, 'F1 score': 0.8023340190150989}, 'tfidf': {'Accuracy': 0.8674466959416517, 'Precision': 0.9183608839699641, 'Recall': 0.807952645593128, 'F1 score': 0.8669815743454972}}, 'KNeighborsClassifier': {'vectorize': {'Accuracy': 0.68961086739971, 'Precision': 0.6848624380838427, 'Recall': 0.7016803421641996, 'F1 score': 0.6886435558704466}, 'tfidf': {'Accuracy': 0.7821538174722555, 'Precision': 0.746527932174623, 'Recall': 0.8645367143864426, 'F1 score': 0.778747803085319}}, 'DecisionTreeClassifier': {'vectorize': {'Accuracy': 0.7392564435433862, 'Precision': 0.7500520274235448, 'Recall': 0.7177803620405638, 'F1 score': 0.738957971700734}, 'tfidf': {'Accuracy': 0.7515306058641958, 'Precision': 0.7570337439640518, 'Recall': 0.741911725167869, 'F1 score': 0.7514837976593478}}, 'RandomForestClassifier': {'vectorize': {'Accuracy': 0.8230991180868173, 'Precision': 0.8546001093214466, 'Recall': 0.7809209004450783, 'F1 score': 0.8225032192740823}, 'tfidf': {'Accuracy': 0.8316864440039333, 'Precision': 0.8923747673267952, 'Recall': 0.7552188673055581, 'F1 score': 0.8303715296456406}}, 'GradientBoostingClassifier': {'vectorize': {'Accuracy': 0.8015507090616344, 'Precision': 0.8688816803779094, 'Recall': 0.7107787740346186, 'F1 score': 0.7992863713248755}, 'tfidf': {'Accuracy': 0.8060895705083088, 'Precision': 0.8616715131095543, 'Recall': 0.7305838077405757, 'F1 score': 0.8045632062296771}}}
Best accuracy: 0.8674466959416517
Best model: SVC
ŠŃŠµŠæŃŠ¾ŃŠµŃŃŠøŠ½Š³ ŃŠµŠŗŃŃŠ¾Š²Ā¶
InĀ [19]:
import nltk
from nltk.corpus import stopwords
from string import punctuation
nltk.download("stopwords")
stop_words = stopwords.words("english")
[nltk_data] Downloading package stopwords to /home/tech/nltk_data... [nltk_data] Package stopwords is already up-to-date!
InĀ [20]:
def remove_punctuation(text):
translator = str.maketrans(punctuation, ' ' * len(punctuation))
return text.translate(translator)
def text_preprocessing(text):
text = re.sub(r'https?://\S+|www\.\S+', ' ', text)
text = re.sub(r'<.*?>', ' ', text)
text = re.sub(r'{[^>]+}', ' ', text)
emoji_pattern = re.compile("["
u"\U0001F600-\U0001F64F"
u"\U0001F300-\U0001F5FF"
u"\U0001F680-\U0001F6FF"
u"\U0001F1E0-\U0001F1FF"
u"\U00002702-\U000027B0"
u"\U000024C2-\U0001F251"
"]+", flags=re.UNICODE)
text = emoji_pattern.sub(r'', text)
text = re.sub(r'\S*@\S*\s?', ' ', text)
text = re.sub(r'[0-9]+', ' ', text)
text = re.sub(r'-|Ā«|Ā»', ' ', text)
text = re.sub(r'[^\w\s]', ' ', text)
text = re.sub(r'\n', ' ', text)
text = text.lower()
text = remove_punctuation(text)
text = text.strip()
# text = ' '.join([word for i, word in enumerate(text.split()) if word not in stop_words and i < 1000])
return text
InĀ [21]:
def tfidf(df: pd.DataFrame):
tfidf = TfidfVectorizer()
return np.array(tfidf.fit_transform([text_preprocessing(t) for t in df['text'].values]).todense())
def vectorize(df: pd.DataFrame):
count_vec = CountVectorizer()
return np.array(count_vec.fit_transform([text_preprocessing(t) for t in df['text'].values]).todense())
InĀ [22]:
models = [MultinomialNB, LogisticRegression, SVC, KNeighborsClassifier, DecisionTreeClassifier, RandomForestClassifier, GradientBoostingClassifier]
for dataset in chosen_datasets:
model_trainer = ModelTrainer(dataset, "train_preprocessing")
print(f"Start processing {dataset}")
for i, model in enumerate(models):
model_trainer.train(model(), vectorize)
model_trainer.train(model(), tfidf)
print(f"Processed model {i + 1}/{len(models)}")
model_trainer.save_train_info_into_pickle()
Start processing kate_symes.csv
Processed model 1/7
Processed model 2/7
Processed model 3/7
Processed model 4/7
Processed model 5/7
Processed model 6/7
Processed model 7/7 Start processing sally_beck.csv
Processed model 1/7
Processed model 2/7
Processed model 3/7
Processed model 4/7
Processed model 5/7
Processed model 6/7
Processed model 7/7 Start processing carol_clair.csv
Processed model 1/7
Processed model 2/7
Processed model 3/7
Processed model 4/7
Processed model 5/7
Processed model 6/7
Processed model 7/7 Start processing michelle_cash.csv
Processed model 1/7
Processed model 2/7
Processed model 3/7
Processed model 4/7
Processed model 5/7
Processed model 6/7
Processed model 7/7 Start processing chris_germany.csv
Processed model 1/7
Processed model 2/7
Processed model 3/7
Processed model 4/7
Processed model 5/7
Processed model 6/7
Processed model 7/7
InĀ [23]:
analyze_train_data("train_preprocessing")
{'MultinomialNB': {'vectorize': {'Accuracy': 0.8555801217129735, 'Precision': 0.8139409518887663, 'Recall': 0.9245149540068385, 'F1 score': 0.8546369045723674}, 'tfidf': {'Accuracy': 0.805711771819829, 'Precision': 0.7497656089951071, 'Recall': 0.9368422856009818, 'F1 score': 0.8004417045882993}}, 'LogisticRegression': {'vectorize': {'Accuracy': 0.8494239397860139, 'Precision': 0.8694290360207562, 'Recall': 0.825392056694404, 'F1 score': 0.8493003647608818}, 'tfidf': {'Accuracy': 0.8639362147507985, 'Precision': 0.8983786086857561, 'Recall': 0.8223142343497493, 'F1 score': 0.8636745221843342}}, 'SVC': {'vectorize': {'Accuracy': 0.8053302160173595, 'Precision': 0.8724682750273394, 'Recall': 0.7167567551611785, 'F1 score': 0.8036935151103324}, 'tfidf': {'Accuracy': 0.8656147502400435, 'Precision': 0.9164025699060454, 'Recall': 0.8057804454415356, 'F1 score': 0.8651398228199018}}, 'KNeighborsClassifier': {'vectorize': {'Accuracy': 0.6913857446916231, 'Precision': 0.705160014054423, 'Recall': 0.6651352571183408, 'F1 score': 0.6906547256723178}, 'tfidf': {'Accuracy': 0.5301185899508135, 'Precision': 0.9387420743277641, 'Recall': 0.06396262731005695, 'F1 score': 0.39887571528408194}}, 'DecisionTreeClassifier': {'vectorize': {'Accuracy': 0.7466444802754547, 'Precision': 0.7600072667498627, 'Recall': 0.7239641790139617, 'F1 score': 0.7464255850580487}, 'tfidf': {'Accuracy': 0.7368277600718532, 'Precision': 0.7335595656105338, 'Recall': 0.7463206720892861, 'F1 score': 0.7367496892587942}}, 'RandomForestClassifier': {'vectorize': {'Accuracy': 0.8211837169771178, 'Precision': 0.840341547654951, 'Recall': 0.7951534224186275, 'F1 score': 0.820948914305575}, 'tfidf': {'Accuracy': 0.8422595162914532, 'Precision': 0.8897072383867254, 'Recall': 0.7817952534054547, 'F1 score': 0.8415195730159045}}, 'GradientBoostingClassifier': {'vectorize': {'Accuracy': 0.8006411297013247, 'Precision': 0.8654985051683781, 'Recall': 0.7119088943102003, 'F1 score': 0.7984846950365808}, 'tfidf': {'Accuracy': 0.8042680342174521, 'Precision': 0.8572903625638177, 'Recall': 0.7313207765968069, 'F1 score': 0.8028806431140139}}}
Best accuracy: 0.8656147502400435
Best model: SVC
ŠŃŃŠ»ŠµŠ“Š¾Š²Š°Š½ŠøŠµ ŠæŃŠµŠæŃŠ¾ŃŠµŃŃŠøŠ½Š³Š° Š½Š° Š»ŃŃŃŠµŠ¹ Š¼Š¾Š“ŠµŠ»Šø SVM
InĀ [24]:
# Raw 'Accuracy': 0.864054582489543
# Preprocessing without lemmatization 'Accuracy': 0.8566528760292869
# Save stop words + without lemmatization 0.8605192309990312
# Lemmatization + save stop words 0.8605192309990312
# Only Lower text 0.8605459869746198
Š ŠøŃŠ¾Š³Šµ, Š»ŃŃŃŠµ Š²ŃŠµŠ³Š¾ Š¼Š¾Š“ŠµŠ»Ń ŃŠ°Š±Š¾ŃŠ°ŠµŃ Š½Š° Š¾Š±ŃŃŠ½Š¾Š¼ ŃŠµŠŗŃŃŠµ.
ŠŠµŠ¹ŃŠ¾ŃŠµŃŠøĀ¶
InĀ [25]:
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, SimpleRNN, Dense
tf.config.list_physical_devices('GPU')
2024-04-04 14:50:08.442048: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-04 14:50:08.928271: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-04-04 14:50:09.330644: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-04-04 14:50:09.349095: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. Skipping registering GPU devices...
Out[25]:
[]
InĀ [26]:
def print_metrics(history, epochs=2):
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs_range = range(epochs)
plt.figure(figsize=(15, 8))
plt.subplot(1, 3, 1)
plt.plot(epochs_range, acc, label='Train')
plt.plot(epochs_range, val_acc, label='Test')
plt.legend()
plt.grid()
plt.title('Accuracy')
plt.subplot(1, 3, 2)
plt.plot(epochs_range, loss, label='Train')
plt.plot(epochs_range, val_loss, label='Test')
plt.legend()
plt.grid()
plt.title('Loss')
plt.show()
def print_conf_matrix(labels, pred_labels):
cm = ConfusionMatrixDisplay(confusion_matrix(labels, pred_labels))
cm.plot()
plt.show()
InĀ [27]:
accuracy = []
precision = []
recall = []
for dataset in chosen_datasets:
df = pd.read_csv(os.path.join("datasets", dataset))
X_train, X_test, y_train, y_test = train_test_split(df["text"].values, df["label"].values, test_size=0.25, random_state=1)
tokenizer = Tokenizer()
tokenizer.fit_on_texts(X_train)
sequences = tokenizer.texts_to_sequences(X_train)
max_length = max([len(seq) for seq in sequences])
padded_sequences = pad_sequences(sequences, maxlen=max_length)
new_sequences = tokenizer.texts_to_sequences(X_test)
new_padded_sequences = pad_sequences(new_sequences, maxlen=max_length)
model = Sequential()
model.add(Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=32))
model.add(SimpleRNN(32))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
epochs = 3
history = model.fit(padded_sequences, y_train, validation_data=(new_padded_sequences, y_test), epochs=epochs)
print_metrics(history, epochs=epochs)
predictions = model.predict(new_padded_sequences)
correct = 0
predicted_labels = []
for i in range(len(predictions)):
prediction_label = 1 if predictions[i] > 0.5 else 0
predicted_labels.append(prediction_label)
if y_test[i] == prediction_label:
correct += 1
print_conf_matrix(y_test, predicted_labels)
accuracy.append(correct / len(predictions))
print(f"Accuracy: {correct / len(predictions)}")
print(f"Precision: {precision_score(y_test, predicted_labels)}")
precision.append(precision_score(y_test, predicted_labels))
print(f"Recall: {recall_score(y_test, predicted_labels)}")
recall.append(recall_score(y_test, predicted_labels))
print(f"Average accuracy: {np.average(accuracy)}")
print(f"Average precision: {np.average(precision)}")
print(f"Average recall: {np.average(recall)}")
Epoch 1/3
1/60 āāāāāāāāāāāāāāāāāāāā 58s 994ms/step - accuracy: 0.6562 - loss: 0.6730
2/60 āāāāāāāāāāāāāāāāāāāā 9s 163ms/step - accuracy: 0.6641 - loss: 0.6725
3/60 āāāāāāāāāāāāāāāāāāāā 9s 161ms/step - accuracy: 0.6337 - loss: 0.6784
4/60 āāāāāāāāāāāāāāāāāāāā 9s 162ms/step - accuracy: 0.6178 - loss: 0.6816
5/60 āāāāāāāāāāāāāāāāāāāā 9s 164ms/step - accuracy: 0.6055 - loss: 0.6837
6/60 āāāāāāāāāāāāāāāāāāāā 8s 164ms/step - accuracy: 0.5984 - loss: 0.6850
7/60 āāāāāāāāāāāāāāāāāāāā 8s 165ms/step - accuracy: 0.5951 - loss: 0.6858
8/60 āāāāāāāāāāāāāāāāāāāā 8s 165ms/step - accuracy: 0.5925 - loss: 0.6865
9/60 āāāāāāāāāāāāāāāāāāāā 8s 165ms/step - accuracy: 0.5888 - loss: 0.6873
10/60 āāāāāāāāāāāāāāāāāāāā 8s 166ms/step - accuracy: 0.5862 - loss: 0.6877
11/60 āāāāāāāāāāāāāāāāāāāā 8s 164ms/step - accuracy: 0.5845 - loss: 0.6879
12/60 āāāāāāāāāāāāāāāāāāāā 7s 163ms/step - accuracy: 0.5834 - loss: 0.6879
13/60 āāāāāāāāāāāāāāāāāāāā 7s 163ms/step - accuracy: 0.5825 - loss: 0.6877
14/60 āāāāāāāāāāāāāāāāāāāā 7s 163ms/step - accuracy: 0.5819 - loss: 0.6875
15/60 āāāāāāāāāāāāāāāāāāāā 7s 163ms/step - accuracy: 0.5808 - loss: 0.6875
16/60 āāāāāāāāāāāāāāāāāāāā 7s 164ms/step - accuracy: 0.5802 - loss: 0.6874
17/60 āāāāāāāāāāāāāāāāāāāā 6s 163ms/step - accuracy: 0.5799 - loss: 0.6872
18/60 āāāāāāāāāāāāāāāāāāāā 6s 162ms/step - accuracy: 0.5793 - loss: 0.6872
19/60 āāāāāāāāāāāāāāāāāāāā 6s 162ms/step - accuracy: 0.5784 - loss: 0.6872
20/60 āāāāāāāāāāāāāāāāāāāā 6s 162ms/step - accuracy: 0.5776 - loss: 0.6872
21/60 āāāāāāāāāāāāāāāāāāāā 6s 161ms/step - accuracy: 0.5768 - loss: 0.6872
22/60 āāāāāāāāāāāāāāāāāāāā 6s 161ms/step - accuracy: 0.5761 - loss: 0.6872
23/60 āāāāāāāāāāāāāāāāāāāā 5s 160ms/step - accuracy: 0.5755 - loss: 0.6871
24/60 āāāāāāāāāāāāāāāāāāāā 5s 160ms/step - accuracy: 0.5751 - loss: 0.6870
25/60 āāāāāāāāāāāāāāāāāāāā 5s 160ms/step - accuracy: 0.5746 - loss: 0.6869
26/60 āāāāāāāāāāāāāāāāāāāā 5s 160ms/step - accuracy: 0.5741 - loss: 0.6868
27/60 āāāāāāāāāāāāāāāāāāāā 5s 160ms/step - accuracy: 0.5737 - loss: 0.6867
28/60 āāāāāāāāāāāāāāāāāāāā 5s 160ms/step - accuracy: 0.5734 - loss: 0.6866
29/60 āāāāāāāāāāāāāāāāāāāā 4s 160ms/step - accuracy: 0.5731 - loss: 0.6865
30/60 āāāāāāāāāāāāāāāāāāāā 4s 160ms/step - accuracy: 0.5728 - loss: 0.6864
31/60 āāāāāāāāāāāāāāāāāāāā 4s 160ms/step - accuracy: 0.5725 - loss: 0.6863
32/60 āāāāāāāāāāāāāāāāāāāā 4s 160ms/step - accuracy: 0.5722 - loss: 0.6862
33/60 āāāāāāāāāāāāāāāāāāāā 4s 160ms/step - accuracy: 0.5720 - loss: 0.6861
34/60 āāāāāāāāāāāāāāāāāāāā 4s 160ms/step - accuracy: 0.5719 - loss: 0.6860
35/60 āāāāāāāāāāāāāāāāāāāā 3s 160ms/step - accuracy: 0.5718 - loss: 0.6859
36/60 āāāāāāāāāāāāāāāāāāāā 3s 160ms/step - accuracy: 0.5718 - loss: 0.6857
37/60 āāāāāāāāāāāāāāāāāāāā 3s 160ms/step - accuracy: 0.5719 - loss: 0.6856
38/60 āāāāāāāāāāāāāāāāāāāā 3s 160ms/step - accuracy: 0.5719 - loss: 0.6854
39/60 āāāāāāāāāāāāāāāāāāāā 3s 160ms/step - accuracy: 0.5720 - loss: 0.6852
40/60 āāāāāāāāāāāāāāāāāāāā 3s 160ms/step - accuracy: 0.5722 - loss: 0.6850
41/60 āāāāāāāāāāāāāāāāāāāā 3s 160ms/step - accuracy: 0.5723 - loss: 0.6848
42/60 āāāāāāāāāāāāāāāāāāāā 2s 160ms/step - accuracy: 0.5725 - loss: 0.6846
43/60 āāāāāāāāāāāāāāāāāāāā 2s 160ms/step - accuracy: 0.5727 - loss: 0.6843
44/60 āāāāāāāāāāāāāāāāāāāā 2s 160ms/step - accuracy: 0.5730 - loss: 0.6840
45/60 āāāāāāāāāāāāāāāāāāāā 2s 160ms/step - accuracy: 0.5733 - loss: 0.6838
46/60 āāāāāāāāāāāāāāāāāāāā 2s 160ms/step - accuracy: 0.5737 - loss: 0.6835
47/60 āāāāāāāāāāāāāāāāāāāā 2s 160ms/step - accuracy: 0.5741 - loss: 0.6832
48/60 āāāāāāāāāāāāāāāāāāāā 1s 160ms/step - accuracy: 0.5744 - loss: 0.6829
49/60 āāāāāāāāāāāāāāāāāāāā 1s 160ms/step - accuracy: 0.5748 - loss: 0.6826
50/60 āāāāāāāāāāāāāāāāāāāā 1s 160ms/step - accuracy: 0.5752 - loss: 0.6822
51/60 āāāāāāāāāāāāāāāāāāāā 1s 160ms/step - accuracy: 0.5757 - loss: 0.6819
52/60 āāāāāāāāāāāāāāāāāāāā 1s 160ms/step - accuracy: 0.5762 - loss: 0.6815
53/60 āāāāāāāāāāāāāāāāāāāā 1s 160ms/step - accuracy: 0.5767 - loss: 0.6812
54/60 āāāāāāāāāāāāāāāāāāāā 0s 160ms/step - accuracy: 0.5771 - loss: 0.6808
55/60 āāāāāāāāāāāāāāāāāāāā 0s 160ms/step - accuracy: 0.5776 - loss: 0.6805
56/60 āāāāāāāāāāāāāāāāāāāā 0s 160ms/step - accuracy: 0.5781 - loss: 0.6801
57/60 āāāāāāāāāāāāāāāāāāāā 0s 160ms/step - accuracy: 0.5787 - loss: 0.6798
58/60 āāāāāāāāāāāāāāāāāāāā 0s 160ms/step - accuracy: 0.5792 - loss: 0.6794
59/60 āāāāāāāāāāāāāāāāāāāā 0s 160ms/step - accuracy: 0.5797 - loss: 0.6790
60/60 āāāāāāāāāāāāāāāāāāāā 0s 160ms/step - accuracy: 0.5802 - loss: 0.6786
60/60 āāāāāāāāāāāāāāāāāāāā 11s 169ms/step - accuracy: 0.5807 - loss: 0.6782 - val_accuracy: 0.7461 - val_loss: 0.5474
Epoch 2/3
1/60 āāāāāāāāāāāāāāāāāāāā 9s 168ms/step - accuracy: 0.8125 - loss: 0.4853
2/60 āāāāāāāāāāāāāāāāāāāā 9s 163ms/step - accuracy: 0.8125 - loss: 0.4842
3/60 āāāāāāāāāāāāāāāāāāāā 9s 158ms/step - accuracy: 0.8090 - loss: 0.4831
4/60 āāāāāāāāāāāāāāāāāāāā 8s 155ms/step - accuracy: 0.8118 - loss: 0.4783
5/60 āāāāāāāāāāāāāāāāāāāā 8s 158ms/step - accuracy: 0.8057 - loss: 0.4793
6/60 āāāāāāāāāāāāāāāāāāāā 8s 157ms/step - accuracy: 0.8016 - loss: 0.4800
7/60 āāāāāāāāāāāāāāāāāāāā 8s 157ms/step - accuracy: 0.7962 - loss: 0.4819
8/60 āāāāāāāāāāāāāāāāāāāā 8s 157ms/step - accuracy: 0.7924 - loss: 0.4833
9/60 āāāāāāāāāāāāāāāāāāāā 8s 158ms/step - accuracy: 0.7900 - loss: 0.4838
10/60 āāāāāāāāāāāāāāāāāāāā 7s 157ms/step - accuracy: 0.7875 - loss: 0.4843
11/60 āāāāāāāāāāāāāāāāāāāā 7s 157ms/step - accuracy: 0.7844 - loss: 0.4856
12/60 āāāāāāāāāāāāāāāāāāāā 7s 158ms/step - accuracy: 0.7826 - loss: 0.4859
13/60 āāāāāāāāāāāāāāāāāāāā 7s 158ms/step - accuracy: 0.7821 - loss: 0.4852
14/60 āāāāāāāāāāāāāāāāāāāā 7s 159ms/step - accuracy: 0.7821 - loss: 0.4841
15/60 āāāāāāāāāāāāāāāāāāāā 7s 159ms/step - accuracy: 0.7820 - loss: 0.4833
16/60 āāāāāāāāāāāāāāāāāāāā 7s 159ms/step - accuracy: 0.7805 - loss: 0.4842
17/60 āāāāāāāāāāāāāāāāāāāā 6s 160ms/step - accuracy: 0.7787 - loss: 0.4855
18/60 āāāāāāāāāāāāāāāāāāāā 6s 160ms/step - accuracy: 0.7764 - loss: 0.4873
19/60 āāāāāāāāāāāāāāāāāāāā 6s 159ms/step - accuracy: 0.7739 - loss: 0.4892
20/60 āāāāāāāāāāāāāāāāāāāā 6s 159ms/step - accuracy: 0.7716 - loss: 0.4908
21/60 āāāāāāāāāāāāāāāāāāāā 6s 158ms/step - accuracy: 0.7697 - loss: 0.4923
22/60 āāāāāāāāāāāāāāāāāāāā 6s 158ms/step - accuracy: 0.7681 - loss: 0.4935
23/60 āāāāāāāāāāāāāāāāāāāā 5s 158ms/step - accuracy: 0.7671 - loss: 0.4946
24/60 āāāāāāāāāāāāāāāāāāāā 5s 158ms/step - accuracy: 0.7663 - loss: 0.4955
25/60 āāāāāāāāāāāāāāāāāāāā 5s 158ms/step - accuracy: 0.7659 - loss: 0.4964
26/60 āāāāāāāāāāāāāāāāāāāā 5s 158ms/step - accuracy: 0.7658 - loss: 0.4971
27/60 āāāāāāāāāāāāāāāāāāāā 5s 158ms/step - accuracy: 0.7659 - loss: 0.4977
28/60 āāāāāāāāāāāāāāāāāāāā 5s 158ms/step - accuracy: 0.7660 - loss: 0.4982
29/60 āāāāāāāāāāāāāāāāāāāā 4s 158ms/step - accuracy: 0.7663 - loss: 0.4986
30/60 āāāāāāāāāāāāāāāāāāāā 4s 158ms/step - accuracy: 0.7667 - loss: 0.4991
31/60 āāāāāāāāāāāāāāāāāāāā 4s 159ms/step - accuracy: 0.7671 - loss: 0.4995
32/60 āāāāāāāāāāāāāāāāāāāā 4s 159ms/step - accuracy: 0.7675 - loss: 0.4999
33/60 āāāāāāāāāāāāāāāāāāāā 4s 159ms/step - accuracy: 0.7681 - loss: 0.5003
34/60 āāāāāāāāāāāāāāāāāāāā 4s 159ms/step - accuracy: 0.7685 - loss: 0.5007
35/60 āāāāāāāāāāāāāāāāāāāā 3s 158ms/step - accuracy: 0.7691 - loss: 0.5010
36/60 āāāāāāāāāāāāāāāāāāāā 3s 158ms/step - accuracy: 0.7697 - loss: 0.5012
37/60 āāāāāāāāāāāāāāāāāāāā 3s 158ms/step - accuracy: 0.7703 - loss: 0.5015
38/60 āāāāāāāāāāāāāāāāāāāā 3s 158ms/step - accuracy: 0.7708 - loss: 0.5017
39/60 āāāāāāāāāāāāāāāāāāāā 3s 159ms/step - accuracy: 0.7714 - loss: 0.5019
40/60 āāāāāāāāāāāāāāāāāāāā 3s 159ms/step - accuracy: 0.7718 - loss: 0.5021
41/60 āāāāāāāāāāāāāāāāāāāā 3s 159ms/step - accuracy: 0.7723 - loss: 0.5023
42/60 āāāāāāāāāāāāāāāāāāāā 2s 159ms/step - accuracy: 0.7728 - loss: 0.5025
43/60 āāāāāāāāāāāāāāāāāāāā 2s 159ms/step - accuracy: 0.7734 - loss: 0.5026
44/60 āāāāāāāāāāāāāāāāāāāā 2s 159ms/step - accuracy: 0.7740 - loss: 0.5027
45/60 āāāāāāāāāāāāāāāāāāāā 2s 159ms/step - accuracy: 0.7745 - loss: 0.5028
46/60 āāāāāāāāāāāāāāāāāāāā 2s 159ms/step - accuracy: 0.7750 - loss: 0.5030
47/60 āāāāāāāāāāāāāāāāāāāā 2s 159ms/step - accuracy: 0.7755 - loss: 0.5031
48/60 āāāāāāāāāāāāāāāāāāāā 1s 159ms/step - accuracy: 0.7760 - loss: 0.5031
49/60 āāāāāāāāāāāāāāāāāāāā 1s 158ms/step - accuracy: 0.7766 - loss: 0.5032
50/60 āāāāāāāāāāāāāāāāāāāā 1s 158ms/step - accuracy: 0.7772 - loss: 0.5032
51/60 āāāāāāāāāāāāāāāāāāāā 1s 158ms/step - accuracy: 0.7778 - loss: 0.5031
52/60 āāāāāāāāāāāāāāāāāāāā 1s 158ms/step - accuracy: 0.7785 - loss: 0.5031
53/60 āāāāāāāāāāāāāāāāāāāā 1s 158ms/step - accuracy: 0.7791 - loss: 0.5030
54/60 āāāāāāāāāāāāāāāāāāāā 0s 159ms/step - accuracy: 0.7798 - loss: 0.5029
55/60 āāāāāāāāāāāāāāāāāāāā 0s 159ms/step - accuracy: 0.7804 - loss: 0.5028
56/60 āāāāāāāāāāāāāāāāāāāā 0s 158ms/step - accuracy: 0.7811 - loss: 0.5026
57/60 āāāāāāāāāāāāāāāāāāāā 0s 159ms/step - accuracy: 0.7817 - loss: 0.5025
58/60 āāāāāāāāāāāāāāāāāāāā 0s 158ms/step - accuracy: 0.7824 - loss: 0.5024
59/60 āāāāāāāāāāāāāāāāāāāā 0s 158ms/step - accuracy: 0.7830 - loss: 0.5022
60/60 āāāāāāāāāāāāāāāāāāāā 0s 158ms/step - accuracy: 0.7836 - loss: 0.5021
60/60 āāāāāāāāāāāāāāāāāāāā 10s 166ms/step - accuracy: 0.7843 - loss: 0.5019 - val_accuracy: 0.8123 - val_loss: 0.4840
Epoch 3/3
1/60 āāāāāāāāāāāāāāāāāāāā 9s 160ms/step - accuracy: 0.9375 - loss: 0.2871
2/60 āāāāāāāāāāāāāāāāāāāā 8s 153ms/step - accuracy: 0.9219 - loss: 0.3105
3/60 āāāāāāāāāāāāāāāāāāāā 9s 159ms/step - accuracy: 0.9201 - loss: 0.3187
4/60 āāāāāāāāāāāāāāāāāāāā 8s 158ms/step - accuracy: 0.9225 - loss: 0.3226
5/60 āāāāāāāāāāāāāāāāāāāā 8s 161ms/step - accuracy: 0.9230 - loss: 0.3265
6/60 āāāāāāāāāāāāāāāāāāāā 8s 161ms/step - accuracy: 0.9246 - loss: 0.3262
7/60 āāāāāāāāāāāāāāāāāāāā 8s 159ms/step - accuracy: 0.9258 - loss: 0.3254
8/60 āāāāāāāāāāāāāāāāāāāā 8s 160ms/step - accuracy: 0.9272 - loss: 0.3241
9/60 āāāāāāāāāāāāāāāāāāāā 8s 160ms/step - accuracy: 0.9284 - loss: 0.3226
10/60 āāāāāāāāāāāāāāāāāāāā 7s 159ms/step - accuracy: 0.9284 - loss: 0.3215
11/60 āāāāāāāāāāāāāāāāāāāā 7s 159ms/step - accuracy: 0.9289 - loss: 0.3202
12/60 āāāāāāāāāāāāāāāāāāāā 7s 159ms/step - accuracy: 0.9290 - loss: 0.3189
13/60 āāāāāāāāāāāāāāāāāāāā 7s 160ms/step - accuracy: 0.9295 - loss: 0.3172
14/60 āāāāāāāāāāāāāāāāāāāā 7s 160ms/step - accuracy: 0.9300 - loss: 0.3154
15/60 āāāāāāāāāāāāāāāāāāāā 7s 160ms/step - accuracy: 0.9305 - loss: 0.3136
16/60 āāāāāāāāāāāāāāāāāāāā 7s 161ms/step - accuracy: 0.9307 - loss: 0.3121
17/60 āāāāāāāāāāāāāāāāāāāā 6s 161ms/step - accuracy: 0.9310 - loss: 0.3105
18/60 āāāāāāāāāāāāāāāāāāāā 6s 161ms/step - accuracy: 0.9313 - loss: 0.3091
19/60 āāāāāāāāāāāāāāāāāāāā 6s 161ms/step - accuracy: 0.9315 - loss: 0.3076
20/60 āāāāāāāāāāāāāāāāāāāā 6s 161ms/step - accuracy: 0.9317 - loss: 0.3061
21/60 āāāāāāāāāāāāāāāāāāāā 6s 160ms/step - accuracy: 0.9319 - loss: 0.3046
22/60 āāāāāāāāāāāāāāāāāāāā 6s 160ms/step - accuracy: 0.9323 - loss: 0.3031
23/60 āāāāāāāāāāāāāāāāāāāā 5s 160ms/step - accuracy: 0.9325 - loss: 0.3016
24/60 āāāāāāāāāāāāāāāāāāāā 5s 160ms/step - accuracy: 0.9327 - loss: 0.3003
25/60 āāāāāāāāāāāāāāāāāāāā 5s 160ms/step - accuracy: 0.9328 - loss: 0.2989
26/60 āāāāāāāāāāāāāāāāāāāā 5s 161ms/step - accuracy: 0.9330 - loss: 0.2974
27/60 āāāāāāāāāāāāāāāāāāāā 5s 160ms/step - accuracy: 0.9332 - loss: 0.2959
28/60 āāāāāāāāāāāāāāāāāāāā 5s 160ms/step - accuracy: 0.9333 - loss: 0.2946
29/60 āāāāāāāāāāāāāāāāāāāā 4s 160ms/step - accuracy: 0.9333 - loss: 0.2932
30/60 āāāāāāāāāāāāāāāāāāāā 4s 160ms/step - accuracy: 0.9333 - loss: 0.2919
31/60 āāāāāāāāāāāāāāāāāāāā 4s 159ms/step - accuracy: 0.9333 - loss: 0.2906
32/60 āāāāāāāāāāāāāāāāāāāā 4s 159ms/step - accuracy: 0.9333 - loss: 0.2894
33/60 āāāāāāāāāāāāāāāāāāāā 4s 159ms/step - accuracy: 0.9333 - loss: 0.2882
34/60 āāāāāāāāāāāāāāāāāāāā 4s 159ms/step - accuracy: 0.9333 - loss: 0.2870
35/60 āāāāāāāāāāāāāāāāāāāā 3s 159ms/step - accuracy: 0.9334 - loss: 0.2859
36/60 āāāāāāāāāāāāāāāāāāāā 3s 159ms/step - accuracy: 0.9334 - loss: 0.2848
37/60 āāāāāāāāāāāāāāāāāāāā 3s 159ms/step - accuracy: 0.9334 - loss: 0.2838
38/60 āāāāāāāāāāāāāāāāāāāā 3s 160ms/step - accuracy: 0.9334 - loss: 0.2829
39/60 āāāāāāāāāāāāāāāāāāāā 3s 160ms/step - accuracy: 0.9334 - loss: 0.2820
40/60 āāāāāāāāāāāāāāāāāāāā 3s 160ms/step - accuracy: 0.9334 - loss: 0.2811
41/60 āāāāāāāāāāāāāāāāāāāā 3s 160ms/step - accuracy: 0.9334 - loss: 0.2802
42/60 āāāāāāāāāāāāāāāāāāāā 2s 160ms/step - accuracy: 0.9334 - loss: 0.2794
43/60 āāāāāāāāāāāāāāāāāāāā 2s 160ms/step - accuracy: 0.9334 - loss: 0.2786
44/60 āāāāāāāāāāāāāāāāāāāā 2s 160ms/step - accuracy: 0.9334 - loss: 0.2778
45/60 āāāāāāāāāāāāāāāāāāāā 2s 160ms/step - accuracy: 0.9334 - loss: 0.2770
46/60 āāāāāāāāāāāāāāāāāāāā 2s 160ms/step - accuracy: 0.9334 - loss: 0.2763
47/60 āāāāāāāāāāāāāāāāāāāā 2s 160ms/step - accuracy: 0.9334 - loss: 0.2756
48/60 āāāāāāāāāāāāāāāāāāāā 1s 160ms/step - accuracy: 0.9334 - loss: 0.2749
49/60 āāāāāāāāāāāāāāāāāāāā 1s 160ms/step - accuracy: 0.9334 - loss: 0.2742
50/60 āāāāāāāāāāāāāāāāāāāā 1s 160ms/step - accuracy: 0.9334 - loss: 0.2736
51/60 āāāāāāāāāāāāāāāāāāāā 1s 160ms/step - accuracy: 0.9335 - loss: 0.2729
52/60 āāāāāāāāāāāāāāāāāāāā 1s 160ms/step - accuracy: 0.9335 - loss: 0.2722
53/60 āāāāāāāāāāāāāāāāāāāā 1s 160ms/step - accuracy: 0.9336 - loss: 0.2715
54/60 āāāāāāāāāāāāāāāāāāāā 0s 160ms/step - accuracy: 0.9337 - loss: 0.2708
55/60 āāāāāāāāāāāāāāāāāāāā 0s 159ms/step - accuracy: 0.9337 - loss: 0.2701
56/60 āāāāāāāāāāāāāāāāāāāā 0s 159ms/step - accuracy: 0.9337 - loss: 0.2695
57/60 āāāāāāāāāāāāāāāāāāāā 0s 159ms/step - accuracy: 0.9338 - loss: 0.2689
58/60 āāāāāāāāāāāāāāāāāāāā 0s 159ms/step - accuracy: 0.9338 - loss: 0.2683
59/60 āāāāāāāāāāāāāāāāāāāā 0s 159ms/step - accuracy: 0.9338 - loss: 0.2677
60/60 āāāāāāāāāāāāāāāāāāāā 0s 159ms/step - accuracy: 0.9338 - loss: 0.2672
60/60 āāāāāāāāāāāāāāāāāāāā 10s 167ms/step - accuracy: 0.9338 - loss: 0.2667 - val_accuracy: 0.8596 - val_loss: 0.3288
1/20 āāāāāāāāāāāāāāāāāāāā 1s 100ms/step
3/20 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step
6/20 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step
9/20 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step
12/20 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step
15/20 āāāāāāāāāāāāāāāāāāāā 0s 23ms/step
18/20 āāāāāāāāāāāāāāāāāāāā 0s 23ms/step
20/20 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step
20/20 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step
Accuracy: 0.8596214511041009 Precision: 0.8617363344051447 Recall: 0.8535031847133758
Epoch 1/3
1/67 āāāāāāāāāāāāāāāāāāāā 1:01 936ms/step - accuracy: 0.4062 - loss: 0.7055
2/67 āāāāāāāāāāāāāāāāāāāā 9s 139ms/step - accuracy: 0.4297 - loss: 0.7054
3/67 āāāāāāāāāāāāāāāāāāāā 8s 140ms/step - accuracy: 0.4358 - loss: 0.7067
4/67 āāāāāāāāāāāāāāāāāāāā 8s 138ms/step - accuracy: 0.4460 - loss: 0.7057
5/67 āāāāāāāāāāāāāāāāāāāā 8s 138ms/step - accuracy: 0.4593 - loss: 0.7040
6/67 āāāāāāāāāāāāāāāāāāāā 8s 137ms/step - accuracy: 0.4721 - loss: 0.7024
7/67 āāāāāāāāāāāāāāāāāāāā 8s 137ms/step - accuracy: 0.4812 - loss: 0.7015
8/67 āāāāāāāāāāāāāāāāāāāā 8s 137ms/step - accuracy: 0.4914 - loss: 0.7000
9/67 āāāāāāāāāāāāāāāāāāāā 7s 137ms/step - accuracy: 0.4989 - loss: 0.6987
10/67 āāāāāāāāāāāāāāāāāāāā 7s 137ms/step - accuracy: 0.5049 - loss: 0.6979
11/67 āāāāāāāāāāāāāāāāāāāā 7s 137ms/step - accuracy: 0.5102 - loss: 0.6971
12/67 āāāāāāāāāāāāāāāāāāāā 7s 137ms/step - accuracy: 0.5156 - loss: 0.6962
13/67 āāāāāāāāāāāāāāāāāāāā 7s 137ms/step - accuracy: 0.5203 - loss: 0.6954
14/67 āāāāāāāāāāāāāāāāāāāā 7s 137ms/step - accuracy: 0.5246 - loss: 0.6945
15/67 āāāāāāāāāāāāāāāāāāāā 7s 137ms/step - accuracy: 0.5285 - loss: 0.6937
16/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.5321 - loss: 0.6930
17/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.5354 - loss: 0.6923
18/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.5385 - loss: 0.6916
19/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.5415 - loss: 0.6908
20/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.5446 - loss: 0.6899
21/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.5477 - loss: 0.6890
22/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.5508 - loss: 0.6881
23/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.5535 - loss: 0.6873
24/67 āāāāāāāāāāāāāāāāāāāā 5s 137ms/step - accuracy: 0.5558 - loss: 0.6865
25/67 āāāāāāāāāāāāāāāāāāāā 5s 136ms/step - accuracy: 0.5579 - loss: 0.6858
26/67 āāāāāāāāāāāāāāāāāāāā 5s 136ms/step - accuracy: 0.5599 - loss: 0.6851
27/67 āāāāāāāāāāāāāāāāāāāā 5s 136ms/step - accuracy: 0.5618 - loss: 0.6844
28/67 āāāāāāāāāāāāāāāāāāāā 5s 137ms/step - accuracy: 0.5638 - loss: 0.6837
29/67 āāāāāāāāāāāāāāāāāāāā 5s 137ms/step - accuracy: 0.5654 - loss: 0.6831
30/67 āāāāāāāāāāāāāāāāāāāā 5s 137ms/step - accuracy: 0.5670 - loss: 0.6825
31/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.5686 - loss: 0.6818
32/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.5701 - loss: 0.6812
33/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.5715 - loss: 0.6806
34/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.5729 - loss: 0.6801
35/67 āāāāāāāāāāāāāāāāāāāā 4s 136ms/step - accuracy: 0.5742 - loss: 0.6795
36/67 āāāāāāāāāāāāāāāāāāāā 4s 136ms/step - accuracy: 0.5754 - loss: 0.6790
37/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.5764 - loss: 0.6786
38/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.5775 - loss: 0.6781
39/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.5786 - loss: 0.6777
40/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.5796 - loss: 0.6773
41/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.5805 - loss: 0.6769
42/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.5812 - loss: 0.6765
43/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.5820 - loss: 0.6762
44/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.5827 - loss: 0.6759
45/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.5833 - loss: 0.6756
46/67 āāāāāāāāāāāāāāāāāāāā 2s 137ms/step - accuracy: 0.5840 - loss: 0.6754
47/67 āāāāāāāāāāāāāāāāāāāā 2s 136ms/step - accuracy: 0.5846 - loss: 0.6751
48/67 āāāāāāāāāāāāāāāāāāāā 2s 137ms/step - accuracy: 0.5852 - loss: 0.6748
49/67 āāāāāāāāāāāāāāāāāāāā 2s 137ms/step - accuracy: 0.5858 - loss: 0.6746
50/67 āāāāāāāāāāāāāāāāāāāā 2s 137ms/step - accuracy: 0.5863 - loss: 0.6743
51/67 āāāāāāāāāāāāāāāāāāāā 2s 136ms/step - accuracy: 0.5869 - loss: 0.6740
52/67 āāāāāāāāāāāāāāāāāāāā 2s 136ms/step - accuracy: 0.5874 - loss: 0.6738
53/67 āāāāāāāāāāāāāāāāāāāā 1s 136ms/step - accuracy: 0.5879 - loss: 0.6735
54/67 āāāāāāāāāāāāāāāāāāāā 1s 136ms/step - accuracy: 0.5884 - loss: 0.6732
55/67 āāāāāāāāāāāāāāāāāāāā 1s 136ms/step - accuracy: 0.5888 - loss: 0.6730
56/67 āāāāāāāāāāāāāāāāāāāā 1s 136ms/step - accuracy: 0.5893 - loss: 0.6727
57/67 āāāāāāāāāāāāāāāāāāāā 1s 136ms/step - accuracy: 0.5898 - loss: 0.6725
58/67 āāāāāāāāāāāāāāāāāāāā 1s 136ms/step - accuracy: 0.5903 - loss: 0.6722
59/67 āāāāāāāāāāāāāāāāāāāā 1s 136ms/step - accuracy: 0.5908 - loss: 0.6719
60/67 āāāāāāāāāāāāāāāāāāāā 0s 136ms/step - accuracy: 0.5913 - loss: 0.6716
61/67 āāāāāāāāāāāāāāāāāāāā 0s 136ms/step - accuracy: 0.5918 - loss: 0.6714
62/67 āāāāāāāāāāāāāāāāāāāā 0s 136ms/step - accuracy: 0.5923 - loss: 0.6711
63/67 āāāāāāāāāāāāāāāāāāāā 0s 136ms/step - accuracy: 0.5927 - loss: 0.6708
64/67 āāāāāāāāāāāāāāāāāāāā 0s 136ms/step - accuracy: 0.5932 - loss: 0.6706
65/67 āāāāāāāāāāāāāāāāāāāā 0s 136ms/step - accuracy: 0.5937 - loss: 0.6703
66/67 āāāāāāāāāāāāāāāāāāāā 0s 136ms/step - accuracy: 0.5942 - loss: 0.6700
67/67 āāāāāāāāāāāāāāāāāāāā 0s 136ms/step - accuracy: 0.5946 - loss: 0.6697
67/67 āāāāāāāāāāāāāāāāāāāā 11s 145ms/step - accuracy: 0.5951 - loss: 0.6695 - val_accuracy: 0.6879 - val_loss: 0.6049
Epoch 2/3
1/67 āāāāāāāāāāāāāāāāāāāā 9s 145ms/step - accuracy: 0.5625 - loss: 0.6655
2/67 āāāāāāāāāāāāāāāāāāāā 9s 139ms/step - accuracy: 0.5938 - loss: 0.6447
3/67 āāāāāāāāāāāāāāāāāāāā 8s 140ms/step - accuracy: 0.6111 - loss: 0.6347
4/67 āāāāāāāāāāāāāāāāāāāā 8s 138ms/step - accuracy: 0.6243 - loss: 0.6253
5/67 āāāāāāāāāāāāāāāāāāāā 8s 138ms/step - accuracy: 0.6332 - loss: 0.6189
6/67 āāāāāāāāāāāāāāāāāāāā 8s 137ms/step - accuracy: 0.6353 - loss: 0.6162
7/67 āāāāāāāāāāāāāāāāāāāā 8s 136ms/step - accuracy: 0.6390 - loss: 0.6133
8/67 āāāāāāāāāāāāāāāāāāāā 8s 137ms/step - accuracy: 0.6426 - loss: 0.6107
9/67 āāāāāāāāāāāāāāāāāāāā 7s 137ms/step - accuracy: 0.6464 - loss: 0.6080
10/67 āāāāāāāāāāāāāāāāāāāā 7s 137ms/step - accuracy: 0.6502 - loss: 0.6055
11/67 āāāāāāāāāāāāāāāāāāāā 7s 137ms/step - accuracy: 0.6533 - loss: 0.6031
12/67 āāāāāāāāāāāāāāāāāāāā 7s 137ms/step - accuracy: 0.6568 - loss: 0.6009
13/67 āāāāāāāāāāāāāāāāāāāā 7s 136ms/step - accuracy: 0.6605 - loss: 0.5988
14/67 āāāāāāāāāāāāāāāāāāāā 7s 137ms/step - accuracy: 0.6632 - loss: 0.5970
15/67 āāāāāāāāāāāāāāāāāāāā 7s 137ms/step - accuracy: 0.6659 - loss: 0.5954
16/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.6686 - loss: 0.5938
17/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.6715 - loss: 0.5922
18/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.6744 - loss: 0.5907
19/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.6772 - loss: 0.5893
20/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.6801 - loss: 0.5879
21/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.6829 - loss: 0.5866
22/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.6854 - loss: 0.5854
23/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.6877 - loss: 0.5843
24/67 āāāāāāāāāāāāāāāāāāāā 5s 137ms/step - accuracy: 0.6902 - loss: 0.5831
25/67 āāāāāāāāāāāāāāāāāāāā 5s 137ms/step - accuracy: 0.6926 - loss: 0.5819
26/67 āāāāāāāāāāāāāāāāāāāā 5s 137ms/step - accuracy: 0.6950 - loss: 0.5808
27/67 āāāāāāāāāāāāāāāāāāāā 5s 137ms/step - accuracy: 0.6972 - loss: 0.5799
28/67 āāāāāāāāāāāāāāāāāāāā 5s 137ms/step - accuracy: 0.6993 - loss: 0.5789
29/67 āāāāāāāāāāāāāāāāāāāā 5s 137ms/step - accuracy: 0.7012 - loss: 0.5780
30/67 āāāāāāāāāāāāāāāāāāāā 5s 137ms/step - accuracy: 0.7033 - loss: 0.5771
31/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.7053 - loss: 0.5762
32/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.7072 - loss: 0.5753
33/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.7091 - loss: 0.5743
34/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.7108 - loss: 0.5734
35/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.7125 - loss: 0.5726
36/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.7141 - loss: 0.5716
37/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.7158 - loss: 0.5707
38/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.7173 - loss: 0.5698
39/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.7188 - loss: 0.5689
40/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.7201 - loss: 0.5681
41/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.7215 - loss: 0.5671
42/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.7227 - loss: 0.5663
43/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.7239 - loss: 0.5654
44/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.7251 - loss: 0.5645
45/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.7262 - loss: 0.5637
46/67 āāāāāāāāāāāāāāāāāāāā 2s 137ms/step - accuracy: 0.7272 - loss: 0.5628
47/67 āāāāāāāāāāāāāāāāāāāā 2s 137ms/step - accuracy: 0.7282 - loss: 0.5620
48/67 āāāāāāāāāāāāāāāāāāāā 2s 137ms/step - accuracy: 0.7291 - loss: 0.5612
49/67 āāāāāāāāāāāāāāāāāāāā 2s 137ms/step - accuracy: 0.7300 - loss: 0.5605
50/67 āāāāāāāāāāāāāāāāāāāā 2s 137ms/step - accuracy: 0.7309 - loss: 0.5597
51/67 āāāāāāāāāāāāāāāāāāāā 2s 137ms/step - accuracy: 0.7317 - loss: 0.5589
52/67 āāāāāāāāāāāāāāāāāāāā 2s 137ms/step - accuracy: 0.7325 - loss: 0.5582
53/67 āāāāāāāāāāāāāāāāāāāā 1s 137ms/step - accuracy: 0.7333 - loss: 0.5575
54/67 āāāāāāāāāāāāāāāāāāāā 1s 137ms/step - accuracy: 0.7341 - loss: 0.5567
55/67 āāāāāāāāāāāāāāāāāāāā 1s 137ms/step - accuracy: 0.7349 - loss: 0.5560
56/67 āāāāāāāāāāāāāāāāāāāā 1s 137ms/step - accuracy: 0.7356 - loss: 0.5553
57/67 āāāāāāāāāāāāāāāāāāāā 1s 137ms/step - accuracy: 0.7363 - loss: 0.5545
58/67 āāāāāāāāāāāāāāāāāāāā 1s 137ms/step - accuracy: 0.7370 - loss: 0.5538
59/67 āāāāāāāāāāāāāāāāāāāā 1s 137ms/step - accuracy: 0.7377 - loss: 0.5531
60/67 āāāāāāāāāāāāāāāāāāāā 0s 137ms/step - accuracy: 0.7384 - loss: 0.5524
61/67 āāāāāāāāāāāāāāāāāāāā 0s 137ms/step - accuracy: 0.7391 - loss: 0.5517
62/67 āāāāāāāāāāāāāāāāāāāā 0s 137ms/step - accuracy: 0.7397 - loss: 0.5510
63/67 āāāāāāāāāāāāāāāāāāāā 0s 137ms/step - accuracy: 0.7404 - loss: 0.5502
64/67 āāāāāāāāāāāāāāāāāāāā 0s 137ms/step - accuracy: 0.7411 - loss: 0.5495
65/67 āāāāāāāāāāāāāāāāāāāā 0s 137ms/step - accuracy: 0.7418 - loss: 0.5488
66/67 āāāāāāāāāāāāāāāāāāāā 0s 137ms/step - accuracy: 0.7424 - loss: 0.5481
67/67 āāāāāāāāāāāāāāāāāāāā 0s 137ms/step - accuracy: 0.7431 - loss: 0.5474
67/67 āāāāāāāāāāāāāāāāāāāā 10s 144ms/step - accuracy: 0.7437 - loss: 0.5467 - val_accuracy: 0.7844 - val_loss: 0.4640
Epoch 3/3
1/67 āāāāāāāāāāāāāāāāāāāā 9s 144ms/step - accuracy: 0.8750 - loss: 0.3201
2/67 āāāāāāāāāāāāāāāāāāāā 9s 141ms/step - accuracy: 0.8828 - loss: 0.3121
3/67 āāāāāāāāāāāāāāāāāāāā 8s 140ms/step - accuracy: 0.8941 - loss: 0.3045
4/67 āāāāāāāāāāāāāāāāāāāā 8s 140ms/step - accuracy: 0.8952 - loss: 0.3039
5/67 āāāāāāāāāāāāāāāāāāāā 8s 139ms/step - accuracy: 0.8986 - loss: 0.3012
6/67 āāāāāāāāāāāāāāāāāāāā 8s 138ms/step - accuracy: 0.8999 - loss: 0.3012
7/67 āāāāāāāāāāāāāāāāāāāā 8s 139ms/step - accuracy: 0.9021 - loss: 0.3006
8/67 āāāāāāāāāāāāāāāāāāāā 8s 139ms/step - accuracy: 0.9031 - loss: 0.3006
9/67 āāāāāāāāāāāāāāāāāāāā 8s 139ms/step - accuracy: 0.9050 - loss: 0.3002
10/67 āāāāāāāāāāāāāāāāāāāā 7s 139ms/step - accuracy: 0.9067 - loss: 0.2999
11/67 āāāāāāāāāāāāāāāāāāāā 7s 139ms/step - accuracy: 0.9085 - loss: 0.2993
12/67 āāāāāāāāāāāāāāāāāāāā 7s 138ms/step - accuracy: 0.9096 - loss: 0.2990
13/67 āāāāāāāāāāāāāāāāāāāā 7s 138ms/step - accuracy: 0.9104 - loss: 0.2986
14/67 āāāāāāāāāāāāāāāāāāāā 7s 138ms/step - accuracy: 0.9111 - loss: 0.2984
15/67 āāāāāāāāāāāāāāāāāāāā 7s 138ms/step - accuracy: 0.9117 - loss: 0.2984
16/67 āāāāāāāāāāāāāāāāāāāā 7s 138ms/step - accuracy: 0.9126 - loss: 0.2981
17/67 āāāāāāāāāāāāāāāāāāāā 6s 138ms/step - accuracy: 0.9136 - loss: 0.2976
18/67 āāāāāāāāāāāāāāāāāāāā 6s 138ms/step - accuracy: 0.9145 - loss: 0.2971
19/67 āāāāāāāāāāāāāāāāāāāā 6s 138ms/step - accuracy: 0.9154 - loss: 0.2965
20/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.9161 - loss: 0.2960
21/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.9168 - loss: 0.2955
22/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.9174 - loss: 0.2949
23/67 āāāāāāāāāāāāāāāāāāāā 6s 137ms/step - accuracy: 0.9179 - loss: 0.2944
24/67 āāāāāāāāāāāāāāāāāāāā 5s 138ms/step - accuracy: 0.9182 - loss: 0.2939
25/67 āāāāāāāāāāāāāāāāāāāā 5s 138ms/step - accuracy: 0.9183 - loss: 0.2935
26/67 āāāāāāāāāāāāāāāāāāāā 5s 138ms/step - accuracy: 0.9183 - loss: 0.2932
27/67 āāāāāāāāāāāāāāāāāāāā 5s 138ms/step - accuracy: 0.9180 - loss: 0.2931
28/67 āāāāāāāāāāāāāāāāāāāā 5s 138ms/step - accuracy: 0.9177 - loss: 0.2931
29/67 āāāāāāāāāāāāāāāāāāāā 5s 138ms/step - accuracy: 0.9175 - loss: 0.2930
30/67 āāāāāāāāāāāāāāāāāāāā 5s 137ms/step - accuracy: 0.9173 - loss: 0.2929
31/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.9172 - loss: 0.2928
32/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.9170 - loss: 0.2928
33/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.9169 - loss: 0.2927
34/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.9169 - loss: 0.2926
35/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.9169 - loss: 0.2925
36/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.9169 - loss: 0.2924
37/67 āāāāāāāāāāāāāāāāāāāā 4s 137ms/step - accuracy: 0.9169 - loss: 0.2924
38/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.9169 - loss: 0.2923
39/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.9170 - loss: 0.2921
40/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.9171 - loss: 0.2919
41/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.9172 - loss: 0.2917
42/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.9174 - loss: 0.2915
43/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.9175 - loss: 0.2913
44/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.9177 - loss: 0.2910
45/67 āāāāāāāāāāāāāāāāāāāā 3s 137ms/step - accuracy: 0.9178 - loss: 0.2908
46/67 āāāāāāāāāāāāāāāāāāāā 2s 137ms/step - accuracy: 0.9180 - loss: 0.2905
47/67 āāāāāāāāāāāāāāāāāāāā 2s 137ms/step - accuracy: 0.9182 - loss: 0.2902
48/67 āāāāāāāāāāāāāāāāāāāā 2s 137ms/step - accuracy: 0.9184 - loss: 0.2899
49/67 āāāāāāāāāāāāāāāāāāāā 2s 137ms/step - accuracy: 0.9186 - loss: 0.2896
50/67 āāāāāāāāāāāāāāāāāāāā 2s 137ms/step - accuracy: 0.9188 - loss: 0.2893
51/67 āāāāāāāāāāāāāāāāāāāā 2s 137ms/step - accuracy: 0.9190 - loss: 0.2889
52/67 āāāāāāāāāāāāāāāāāāāā 2s 137ms/step - accuracy: 0.9192 - loss: 0.2886
53/67 āāāāāāāāāāāāāāāāāāāā 1s 137ms/step - accuracy: 0.9193 - loss: 0.2883
54/67 āāāāāāāāāāāāāāāāāāāā 1s 137ms/step - accuracy: 0.9195 - loss: 0.2880
55/67 āāāāāāāāāāāāāāāāāāāā 1s 137ms/step - accuracy: 0.9196 - loss: 0.2876
56/67 āāāāāāāāāāāāāāāāāāāā 1s 137ms/step - accuracy: 0.9198 - loss: 0.2873
57/67 āāāāāāāāāāāāāāāāāāāā 1s 137ms/step - accuracy: 0.9199 - loss: 0.2870
58/67 āāāāāāāāāāāāāāāāāāāā 1s 137ms/step - accuracy: 0.9200 - loss: 0.2867
59/67 āāāāāāāāāāāāāāāāāāāā 1s 137ms/step - accuracy: 0.9201 - loss: 0.2864
60/67 āāāāāāāāāāāāāāāāāāāā 0s 137ms/step - accuracy: 0.9203 - loss: 0.2861
61/67 āāāāāāāāāāāāāāāāāāāā 0s 137ms/step - accuracy: 0.9204 - loss: 0.2858
62/67 āāāāāāāāāāāāāāāāāāāā 0s 137ms/step - accuracy: 0.9205 - loss: 0.2855
63/67 āāāāāāāāāāāāāāāāāāāā 0s 137ms/step - accuracy: 0.9206 - loss: 0.2852
64/67 āāāāāāāāāāāāāāāāāāāā 0s 137ms/step - accuracy: 0.9207 - loss: 0.2849
65/67 āāāāāāāāāāāāāāāāāāāā 0s 137ms/step - accuracy: 0.9208 - loss: 0.2845
66/67 āāāāāāāāāāāāāāāāāāāā 0s 137ms/step - accuracy: 0.9209 - loss: 0.2842
67/67 āāāāāāāāāāāāāāāāāāāā 0s 137ms/step - accuracy: 0.9211 - loss: 0.2838
67/67 āāāāāāāāāāāāāāāāāāāā 10s 144ms/step - accuracy: 0.9212 - loss: 0.2835 - val_accuracy: 0.8411 - val_loss: 0.3738
1/23 āāāāāāāāāāāāāāāāāāāā 1s 88ms/step
4/23 āāāāāāāāāāāāāāāāāāāā 0s 20ms/step
7/23 āāāāāāāāāāāāāāāāāāāā 0s 20ms/step
10/23 āāāāāāāāāāāāāāāāāāāā 0s 20ms/step
13/23 āāāāāāāāāāāāāāāāāāāā 0s 20ms/step
16/23 āāāāāāāāāāāāāāāāāāāā 0s 20ms/step
19/23 āāāāāāāāāāāāāāāāāāāā 0s 20ms/step
22/23 āāāāāāāāāāāāāāāāāāāā 0s 20ms/step
23/23 āāāāāāāāāāāāāāāāāāāā 0s 23ms/step
23/23 āāāāāāāāāāāāāāāāāāāā 1s 23ms/step
Accuracy: 0.8411347517730496 Precision: 0.8433048433048433 Recall: 0.8385269121813032 Epoch 1/3
1/53 āāāāāāāāāāāāāāāāāāāā 46s 896ms/step - accuracy: 0.5625 - loss: 0.6767
2/53 āāāāāāāāāāāāāāāāāāāā 2s 57ms/step - accuracy: 0.6016 - loss: 0.6716
3/53 āāāāāāāāāāāāāāāāāāāā 2s 57ms/step - accuracy: 0.5885 - loss: 0.6735
4/53 āāāāāāāāāāāāāāāāāāāā 2s 56ms/step - accuracy: 0.5879 - loss: 0.6724
5/53 āāāāāāāāāāāāāāāāāāāā 2s 57ms/step - accuracy: 0.5891 - loss: 0.6714
6/53 āāāāāāāāāāāāāāāāāāāā 2s 57ms/step - accuracy: 0.5916 - loss: 0.6697
7/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.5957 - loss: 0.6679
8/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.5984 - loss: 0.6659
9/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.5994 - loss: 0.6647
10/53 āāāāāāāāāāāāāāāāāāāā 2s 59ms/step - accuracy: 0.6014 - loss: 0.6633
11/53 āāāāāāāāāāāāāāāāāāāā 2s 59ms/step - accuracy: 0.6035 - loss: 0.6614
12/53 āāāāāāāāāāāāāāāāāāāā 2s 59ms/step - accuracy: 0.6062 - loss: 0.6590
13/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.6087 - loss: 0.6568
14/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.6113 - loss: 0.6545
15/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.6138 - loss: 0.6526
16/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.6161 - loss: 0.6508
17/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.6185 - loss: 0.6489
18/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.6206 - loss: 0.6472
19/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.6230 - loss: 0.6454
20/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.6251 - loss: 0.6437
21/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.6273 - loss: 0.6421
22/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.6290 - loss: 0.6407
23/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.6303 - loss: 0.6395
24/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.6320 - loss: 0.6381
25/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.6334 - loss: 0.6369
26/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.6347 - loss: 0.6356
27/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.6359 - loss: 0.6344
28/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.6370 - loss: 0.6331
29/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.6381 - loss: 0.6319
30/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.6390 - loss: 0.6309
31/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.6400 - loss: 0.6297
32/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.6410 - loss: 0.6286
33/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.6418 - loss: 0.6275
34/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.6428 - loss: 0.6264
35/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.6437 - loss: 0.6253
36/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.6446 - loss: 0.6242
37/53 āāāāāāāāāāāāāāāāāāāā 0s 59ms/step - accuracy: 0.6455 - loss: 0.6233
38/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.6463 - loss: 0.6223
39/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.6470 - loss: 0.6213
40/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.6478 - loss: 0.6204
41/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.6486 - loss: 0.6195
42/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.6494 - loss: 0.6185
43/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.6502 - loss: 0.6176
44/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.6510 - loss: 0.6166
45/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.6518 - loss: 0.6157
46/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.6525 - loss: 0.6148
47/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.6531 - loss: 0.6140
48/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.6538 - loss: 0.6132
49/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.6545 - loss: 0.6123
50/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.6551 - loss: 0.6116
51/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.6557 - loss: 0.6109
52/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.6563 - loss: 0.6102
53/53 āāāāāāāāāāāāāāāāāāāā 4s 64ms/step - accuracy: 0.6574 - loss: 0.6089 - val_accuracy: 0.6816 - val_loss: 0.5482
Epoch 2/3
1/53 āāāāāāāāāāāāāāāāāāāā 3s 64ms/step - accuracy: 0.8125 - loss: 0.4424
2/53 āāāāāāāāāāāāāāāāāāāā 3s 62ms/step - accuracy: 0.8047 - loss: 0.4498
3/53 āāāāāāāāāāāāāāāāāāāā 3s 60ms/step - accuracy: 0.7969 - loss: 0.4572
4/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.7871 - loss: 0.4627
5/53 āāāāāāāāāāāāāāāāāāāā 2s 57ms/step - accuracy: 0.7859 - loss: 0.4621
6/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.7869 - loss: 0.4621
7/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.7893 - loss: 0.4608
8/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.7922 - loss: 0.4596
9/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.7941 - loss: 0.4588
10/53 āāāāāāāāāāāāāāāāāāāā 2s 59ms/step - accuracy: 0.7956 - loss: 0.4582
11/53 āāāāāāāāāāāāāāāāāāāā 2s 59ms/step - accuracy: 0.7974 - loss: 0.4580
12/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.7995 - loss: 0.4575
13/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.8014 - loss: 0.4569
14/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.8029 - loss: 0.4566
15/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.8039 - loss: 0.4563
16/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.8049 - loss: 0.4558
17/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.8058 - loss: 0.4552
18/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.8068 - loss: 0.4544
19/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.8079 - loss: 0.4534
20/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.8091 - loss: 0.4522
21/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.8105 - loss: 0.4509
22/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.8117 - loss: 0.4498
23/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.8128 - loss: 0.4488
24/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.8138 - loss: 0.4479
25/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.8148 - loss: 0.4470
26/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.8159 - loss: 0.4461
27/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.8169 - loss: 0.4454
28/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.8177 - loss: 0.4448
29/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.8185 - loss: 0.4442
30/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.8194 - loss: 0.4436
31/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.8203 - loss: 0.4428
32/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.8212 - loss: 0.4419
33/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.8221 - loss: 0.4411
34/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.8227 - loss: 0.4403
35/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.8233 - loss: 0.4396
36/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.8239 - loss: 0.4388
37/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.8244 - loss: 0.4381
38/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.8249 - loss: 0.4373
39/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.8255 - loss: 0.4364
40/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.8260 - loss: 0.4356
41/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.8266 - loss: 0.4348
42/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.8271 - loss: 0.4339
43/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.8277 - loss: 0.4331
44/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.8282 - loss: 0.4323
45/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.8287 - loss: 0.4315
46/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.8291 - loss: 0.4308
47/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.8296 - loss: 0.4300
48/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.8300 - loss: 0.4292
49/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.8305 - loss: 0.4285
50/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.8309 - loss: 0.4277
51/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.8313 - loss: 0.4270
52/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.8317 - loss: 0.4263
53/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.8321 - loss: 0.4256
53/53 āāāāāāāāāāāāāāāāāāāā 3s 62ms/step - accuracy: 0.8324 - loss: 0.4249 - val_accuracy: 0.8301 - val_loss: 0.4231
Epoch 3/3
1/53 āāāāāāāāāāāāāāāāāāāā 3s 65ms/step - accuracy: 0.9688 - loss: 0.2422
2/53 āāāāāāāāāāāāāāāāāāāā 3s 60ms/step - accuracy: 0.9531 - loss: 0.2452
3/53 āāāāāāāāāāāāāāāāāāāā 3s 60ms/step - accuracy: 0.9375 - loss: 0.2541
4/53 āāāāāāāāāāāāāāāāāāāā 2s 59ms/step - accuracy: 0.9355 - loss: 0.2505
5/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.9334 - loss: 0.2477
6/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.9315 - loss: 0.2477
7/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.9311 - loss: 0.2460
8/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.9299 - loss: 0.2452
9/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.9288 - loss: 0.2447
10/53 āāāāāāāāāāāāāāāāāāāā 2s 59ms/step - accuracy: 0.9282 - loss: 0.2439
11/53 āāāāāāāāāāāāāāāāāāāā 2s 58ms/step - accuracy: 0.9282 - loss: 0.2425
12/53 āāāāāāāāāāāāāāāāāāāā 2s 59ms/step - accuracy: 0.9279 - loss: 0.2418
13/53 āāāāāāāāāāāāāāāāāāāā 2s 59ms/step - accuracy: 0.9277 - loss: 0.2410
14/53 āāāāāāāāāāāāāāāāāāāā 2s 59ms/step - accuracy: 0.9273 - loss: 0.2406
15/53 āāāāāāāāāāāāāāāāāāāā 2s 59ms/step - accuracy: 0.9272 - loss: 0.2399
16/53 āāāāāāāāāāāāāāāāāāāā 2s 59ms/step - accuracy: 0.9269 - loss: 0.2394
17/53 āāāāāāāāāāāāāāāāāāāā 2s 59ms/step - accuracy: 0.9269 - loss: 0.2388
18/53 āāāāāāāāāāāāāāāāāāāā 2s 59ms/step - accuracy: 0.9269 - loss: 0.2380
19/53 āāāāāāāāāāāāāāāāāāāā 2s 59ms/step - accuracy: 0.9268 - loss: 0.2377
20/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.9267 - loss: 0.2372
21/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.9267 - loss: 0.2367
22/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.9267 - loss: 0.2361
23/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.9267 - loss: 0.2355
24/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.9267 - loss: 0.2350
25/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.9267 - loss: 0.2345
26/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.9267 - loss: 0.2339
27/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.9266 - loss: 0.2335
28/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.9263 - loss: 0.2335
29/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.9260 - loss: 0.2333
30/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.9258 - loss: 0.2332
31/53 āāāāāāāāāāāāāāāāāāāā 1s 59ms/step - accuracy: 0.9257 - loss: 0.2330
32/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.9255 - loss: 0.2328
33/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.9254 - loss: 0.2325
34/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.9253 - loss: 0.2323
35/53 āāāāāāāāāāāāāāāāāāāā 1s 58ms/step - accuracy: 0.9252 - loss: 0.2321
36/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.9252 - loss: 0.2319
37/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.9252 - loss: 0.2317
38/53 āāāāāāāāāāāāāāāāāāāā 0s 59ms/step - accuracy: 0.9252 - loss: 0.2314
39/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.9253 - loss: 0.2311
40/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.9253 - loss: 0.2308
41/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.9254 - loss: 0.2305
42/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.9254 - loss: 0.2302
43/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.9255 - loss: 0.2299
44/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.9255 - loss: 0.2295
45/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.9255 - loss: 0.2293
46/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.9255 - loss: 0.2290
47/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.9256 - loss: 0.2287
48/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.9256 - loss: 0.2284
49/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.9257 - loss: 0.2281
50/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.9258 - loss: 0.2278
51/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.9258 - loss: 0.2275
52/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.9258 - loss: 0.2273
53/53 āāāāāāāāāāāāāāāāāāāā 0s 58ms/step - accuracy: 0.9258 - loss: 0.2270
53/53 āāāāāāāāāāāāāāāāāāāā 3s 62ms/step - accuracy: 0.9259 - loss: 0.2267 - val_accuracy: 0.7496 - val_loss: 0.4795
1/18 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step
6/18 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step
11/18 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step
16/18 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step
18/18 āāāāāāāāāāāāāāāāāāāā 0s 15ms/step
18/18 āāāāāāāāāāāāāāāāāāāā 0s 15ms/step
Accuracy: 0.7495527728085868 Precision: 0.6982248520710059 Recall: 0.8613138686131386
Epoch 1/3
1/48 āāāāāāāāāāāāāāāāāāāā 46s 981ms/step - accuracy: 0.5312 - loss: 0.7010
2/48 āāāāāāāāāāāāāāāāāāāā 6s 149ms/step - accuracy: 0.5312 - loss: 0.6998
3/48 āāāāāāāāāāāāāāāāāāāā 6s 148ms/step - accuracy: 0.5278 - loss: 0.6953
4/48 āāāāāāāāāāāāāāāāāāāā 6s 149ms/step - accuracy: 0.5228 - loss: 0.6938
5/48 āāāāāāāāāāāāāāāāāāāā 6s 150ms/step - accuracy: 0.5307 - loss: 0.6903
6/48 āāāāāāāāāāāāāāāāāāāā 6s 149ms/step - accuracy: 0.5369 - loss: 0.6878
7/48 āāāāāāāāāāāāāāāāāāāā 6s 149ms/step - accuracy: 0.5418 - loss: 0.6858
8/48 āāāāāāāāāāāāāāāāāāāā 5s 149ms/step - accuracy: 0.5469 - loss: 0.6844
9/48 āāāāāāāāāāāāāāāāāāāā 5s 150ms/step - accuracy: 0.5501 - loss: 0.6833
10/48 āāāāāāāāāāāāāāāāāāāā 5s 150ms/step - accuracy: 0.5523 - loss: 0.6824
11/48 āāāāāāāāāāāāāāāāāāāā 5s 149ms/step - accuracy: 0.5548 - loss: 0.6815
12/48 āāāāāāāāāāāāāāāāāāāā 5s 149ms/step - accuracy: 0.5569 - loss: 0.6808
13/48 āāāāāāāāāāāāāāāāāāāā 5s 149ms/step - accuracy: 0.5581 - loss: 0.6804
14/48 āāāāāāāāāāāāāāāāāāāā 5s 150ms/step - accuracy: 0.5595 - loss: 0.6800
15/48 āāāāāāāāāāāāāāāāāāāā 4s 149ms/step - accuracy: 0.5607 - loss: 0.6796
16/48 āāāāāāāāāāāāāāāāāāāā 4s 149ms/step - accuracy: 0.5617 - loss: 0.6794
17/48 āāāāāāāāāāāāāāāāāāāā 4s 149ms/step - accuracy: 0.5629 - loss: 0.6790
18/48 āāāāāāāāāāāāāāāāāāāā 4s 149ms/step - accuracy: 0.5641 - loss: 0.6787
19/48 āāāāāāāāāāāāāāāāāāāā 4s 149ms/step - accuracy: 0.5651 - loss: 0.6783
20/48 āāāāāāāāāāāāāāāāāāāā 4s 150ms/step - accuracy: 0.5662 - loss: 0.6780
21/48 āāāāāāāāāāāāāāāāāāāā 4s 149ms/step - accuracy: 0.5670 - loss: 0.6777
22/48 āāāāāāāāāāāāāāāāāāāā 3s 150ms/step - accuracy: 0.5675 - loss: 0.6774
23/48 āāāāāāāāāāāāāāāāāāāā 3s 150ms/step - accuracy: 0.5678 - loss: 0.6773
24/48 āāāāāāāāāāāāāāāāāāāā 3s 150ms/step - accuracy: 0.5680 - loss: 0.6771
25/48 āāāāāāāāāāāāāāāāāāāā 3s 149ms/step - accuracy: 0.5681 - loss: 0.6770
26/48 āāāāāāāāāāāāāāāāāāāā 3s 150ms/step - accuracy: 0.5683 - loss: 0.6769
27/48 āāāāāāāāāāāāāāāāāāāā 3s 150ms/step - accuracy: 0.5685 - loss: 0.6767
28/48 āāāāāāāāāāāāāāāāāāāā 2s 150ms/step - accuracy: 0.5688 - loss: 0.6766
29/48 āāāāāāāāāāāāāāāāāāāā 2s 150ms/step - accuracy: 0.5690 - loss: 0.6765
30/48 āāāāāāāāāāāāāāāāāāāā 2s 149ms/step - accuracy: 0.5692 - loss: 0.6764
31/48 āāāāāāāāāāāāāāāāāāāā 2s 150ms/step - accuracy: 0.5693 - loss: 0.6763
32/48 āāāāāāāāāāāāāāāāāāāā 2s 149ms/step - accuracy: 0.5695 - loss: 0.6762
33/48 āāāāāāāāāāāāāāāāāāāā 2s 149ms/step - accuracy: 0.5695 - loss: 0.6760
34/48 āāāāāāāāāāāāāāāāāāāā 2s 149ms/step - accuracy: 0.5694 - loss: 0.6759
35/48 āāāāāāāāāāāāāāāāāāāā 1s 149ms/step - accuracy: 0.5693 - loss: 0.6758
36/48 āāāāāāāāāāāāāāāāāāāā 1s 149ms/step - accuracy: 0.5692 - loss: 0.6757
37/48 āāāāāāāāāāāāāāāāāāāā 1s 149ms/step - accuracy: 0.5691 - loss: 0.6756
38/48 āāāāāāāāāāāāāāāāāāāā 1s 149ms/step - accuracy: 0.5692 - loss: 0.6754
39/48 āāāāāāāāāāāāāāāāāāāā 1s 150ms/step - accuracy: 0.5693 - loss: 0.6752
40/48 āāāāāāāāāāāāāāāāāāāā 1s 149ms/step - accuracy: 0.5695 - loss: 0.6750
41/48 āāāāāāāāāāāāāāāāāāāā 1s 149ms/step - accuracy: 0.5698 - loss: 0.6748
42/48 āāāāāāāāāāāāāāāāāāāā 0s 149ms/step - accuracy: 0.5700 - loss: 0.6746
43/48 āāāāāāāāāāāāāāāāāāāā 0s 150ms/step - accuracy: 0.5703 - loss: 0.6744
44/48 āāāāāāāāāāāāāāāāāāāā 0s 150ms/step - accuracy: 0.5707 - loss: 0.6742
45/48 āāāāāāāāāāāāāāāāāāāā 0s 150ms/step - accuracy: 0.5710 - loss: 0.6740
46/48 āāāāāāāāāāāāāāāāāāāā 0s 150ms/step - accuracy: 0.5713 - loss: 0.6738
47/48 āāāāāāāāāāāāāāāāāāāā 0s 150ms/step - accuracy: 0.5716 - loss: 0.6736
48/48 āāāāāāāāāāāāāāāāāāāā 0s 150ms/step - accuracy: 0.5718 - loss: 0.6734
48/48 āāāāāāāāāāāāāāāāāāāā 8s 160ms/step - accuracy: 0.5719 - loss: 0.6732 - val_accuracy: 0.6174 - val_loss: 0.6429
Epoch 2/3
1/48 āāāāāāāāāāāāāāāāāāāā 7s 159ms/step - accuracy: 0.8750 - loss: 0.5380
2/48 āāāāāāāāāāāāāāāāāāāā 6s 147ms/step - accuracy: 0.8281 - loss: 0.5500
3/48 āāāāāāāāāāāāāāāāāāāā 6s 147ms/step - accuracy: 0.8264 - loss: 0.5552
4/48 āāāāāāāāāāāāāāāāāāāā 6s 149ms/step - accuracy: 0.8210 - loss: 0.5585
5/48 āāāāāāāāāāāāāāāāāāāā 6s 150ms/step - accuracy: 0.8205 - loss: 0.5568
6/48 āāāāāāāāāāāāāāāāāāāā 6s 151ms/step - accuracy: 0.8174 - loss: 0.5560
7/48 āāāāāāāāāāāāāāāāāāāā 6s 152ms/step - accuracy: 0.8155 - loss: 0.5550
8/48 āāāāāāāāāāāāāāāāāāāā 6s 151ms/step - accuracy: 0.8136 - loss: 0.5530
9/48 āāāāāāāāāāāāāāāāāāāā 5s 150ms/step - accuracy: 0.8120 - loss: 0.5523
10/48 āāāāāāāāāāāāāāāāāāāā 5s 150ms/step - accuracy: 0.8114 - loss: 0.5518
11/48 āāāāāāāāāāāāāāāāāāāā 5s 150ms/step - accuracy: 0.8107 - loss: 0.5514
12/48 āāāāāāāāāāāāāāāāāāāā 5s 151ms/step - accuracy: 0.8098 - loss: 0.5512
13/48 āāāāāāāāāāāāāāāāāāāā 5s 150ms/step - accuracy: 0.8091 - loss: 0.5509
14/48 āāāāāāāāāāāāāāāāāāāā 5s 150ms/step - accuracy: 0.8084 - loss: 0.5505
15/48 āāāāāāāāāāāāāāāāāāāā 4s 150ms/step - accuracy: 0.8078 - loss: 0.5500
16/48 āāāāāāāāāāāāāāāāāāāā 4s 150ms/step - accuracy: 0.8067 - loss: 0.5498
17/48 āāāāāāāāāāāāāāāāāāāā 4s 150ms/step - accuracy: 0.8052 - loss: 0.5496
18/48 āāāāāāāāāāāāāāāāāāāā 4s 150ms/step - accuracy: 0.8039 - loss: 0.5495
19/48 āāāāāāāāāāāāāāāāāāāā 4s 150ms/step - accuracy: 0.8027 - loss: 0.5493
20/48 āāāāāāāāāāāāāāāāāāāā 4s 150ms/step - accuracy: 0.8014 - loss: 0.5491
21/48 āāāāāāāāāāāāāāāāāāāā 4s 150ms/step - accuracy: 0.8002 - loss: 0.5490
22/48 āāāāāāāāāāāāāāāāāāāā 3s 150ms/step - accuracy: 0.7994 - loss: 0.5489
23/48 āāāāāāāāāāāāāāāāāāāā 3s 150ms/step - accuracy: 0.7989 - loss: 0.5486
24/48 āāāāāāāāāāāāāāāāāāāā 3s 150ms/step - accuracy: 0.7985 - loss: 0.5485
25/48 āāāāāāāāāāāāāāāāāāāā 3s 150ms/step - accuracy: 0.7981 - loss: 0.5483
26/48 āāāāāāāāāāāāāāāāāāāā 3s 150ms/step - accuracy: 0.7975 - loss: 0.5480
27/48 āāāāāāāāāāāāāāāāāāāā 3s 150ms/step - accuracy: 0.7970 - loss: 0.5478
28/48 āāāāāāāāāāāāāāāāāāāā 3s 150ms/step - accuracy: 0.7965 - loss: 0.5475
29/48 āāāāāāāāāāāāāāāāāāāā 2s 150ms/step - accuracy: 0.7962 - loss: 0.5472
30/48 āāāāāāāāāāāāāāāāāāāā 2s 150ms/step - accuracy: 0.7959 - loss: 0.5469
31/48 āāāāāāāāāāāāāāāāāāāā 2s 150ms/step - accuracy: 0.7956 - loss: 0.5466
32/48 āāāāāāāāāāāāāāāāāāāā 2s 150ms/step - accuracy: 0.7954 - loss: 0.5463
33/48 āāāāāāāāāāāāāāāāāāāā 2s 150ms/step - accuracy: 0.7950 - loss: 0.5461
34/48 āāāāāāāāāāāāāāāāāāāā 2s 150ms/step - accuracy: 0.7946 - loss: 0.5458
35/48 āāāāāāāāāāāāāāāāāāāā 1s 150ms/step - accuracy: 0.7943 - loss: 0.5456
36/48 āāāāāāāāāāāāāāāāāāāā 1s 150ms/step - accuracy: 0.7938 - loss: 0.5454
37/48 āāāāāāāāāāāāāāāāāāāā 1s 150ms/step - accuracy: 0.7934 - loss: 0.5452
38/48 āāāāāāāāāāāāāāāāāāāā 1s 150ms/step - accuracy: 0.7930 - loss: 0.5450
39/48 āāāāāāāāāāāāāāāāāāāā 1s 150ms/step - accuracy: 0.7927 - loss: 0.5448
40/48 āāāāāāāāāāāāāāāāāāāā 1s 150ms/step - accuracy: 0.7924 - loss: 0.5446
41/48 āāāāāāāāāāāāāāāāāāāā 1s 150ms/step - accuracy: 0.7921 - loss: 0.5445
42/48 āāāāāāāāāāāāāāāāāāāā 0s 150ms/step - accuracy: 0.7919 - loss: 0.5443
43/48 āāāāāāāāāāāāāāāāāāāā 0s 150ms/step - accuracy: 0.7919 - loss: 0.5440
44/48 āāāāāāāāāāāāāāāāāāāā 0s 150ms/step - accuracy: 0.7918 - loss: 0.5437
45/48 āāāāāāāāāāāāāāāāāāāā 0s 150ms/step - accuracy: 0.7918 - loss: 0.5434
46/48 āāāāāāāāāāāāāāāāāāāā 0s 150ms/step - accuracy: 0.7918 - loss: 0.5430
47/48 āāāāāāāāāāāāāāāāāāāā 0s 150ms/step - accuracy: 0.7918 - loss: 0.5427
48/48 āāāāāāāāāāāāāāāāāāāā 0s 150ms/step - accuracy: 0.7918 - loss: 0.5424
48/48 āāāāāāāāāāāāāāāāāāāā 8s 158ms/step - accuracy: 0.7917 - loss: 0.5422 - val_accuracy: 0.6943 - val_loss: 0.5929
Epoch 3/3
1/48 āāāāāāāāāāāāāāāāāāāā 7s 159ms/step - accuracy: 0.8750 - loss: 0.4933
2/48 āāāāāāāāāāāāāāāāāāāā 6s 149ms/step - accuracy: 0.8906 - loss: 0.4801
3/48 āāāāāāāāāāāāāāāāāāāā 6s 153ms/step - accuracy: 0.8958 - loss: 0.4676
4/48 āāāāāāāāāāāāāāāāāāāā 6s 153ms/step - accuracy: 0.9004 - loss: 0.4635
5/48 āāāāāāāāāāāāāāāāāāāā 6s 154ms/step - accuracy: 0.9053 - loss: 0.4589
6/48 āāāāāāāāāāāāāāāāāāāā 6s 152ms/step - accuracy: 0.9081 - loss: 0.4549
7/48 āāāāāāāāāāāāāāāāāāāā 6s 152ms/step - accuracy: 0.9104 - loss: 0.4514
8/48 āāāāāāāāāāāāāāāāāāāā 6s 151ms/step - accuracy: 0.9118 - loss: 0.4484
9/48 āāāāāāāāāāāāāāāāāāāā 5s 151ms/step - accuracy: 0.9135 - loss: 0.4463
10/48 āāāāāāāāāāāāāāāāāāāā 5s 150ms/step - accuracy: 0.9143 - loss: 0.4447
11/48 āāāāāāāāāāāāāāāāāāāā 5s 151ms/step - accuracy: 0.9157 - loss: 0.4431
12/48 āāāāāāāāāāāāāāāāāāāā 5s 151ms/step - accuracy: 0.9171 - loss: 0.4419
13/48 āāāāāāāāāāāāāāāāāāāā 5s 151ms/step - accuracy: 0.9184 - loss: 0.4407
14/48 āāāāāāāāāāāāāāāāāāāā 5s 152ms/step - accuracy: 0.9196 - loss: 0.4397
15/48 āāāāāāāāāāāāāāāāāāāā 4s 151ms/step - accuracy: 0.9210 - loss: 0.4387
16/48 āāāāāāāāāāāāāāāāāāāā 4s 151ms/step - accuracy: 0.9219 - loss: 0.4380
17/48 āāāāāāāāāāāāāāāāāāāā 4s 151ms/step - accuracy: 0.9226 - loss: 0.4373
18/48 āāāāāāāāāāāāāāāāāāāā 4s 151ms/step - accuracy: 0.9228 - loss: 0.4369
19/48 āāāāāāāāāāāāāāāāāāāā 4s 151ms/step - accuracy: 0.9232 - loss: 0.4364
20/48 āāāāāāāāāāāāāāāāāāāā 4s 151ms/step - accuracy: 0.9234 - loss: 0.4359
21/48 āāāāāāāāāāāāāāāāāāāā 4s 151ms/step - accuracy: 0.9237 - loss: 0.4354
22/48 āāāāāāāāāāāāāāāāāāāā 3s 151ms/step - accuracy: 0.9237 - loss: 0.4350
23/48 āāāāāāāāāāāāāāāāāāāā 3s 152ms/step - accuracy: 0.9238 - loss: 0.4346
24/48 āāāāāāāāāāāāāāāāāāāā 3s 152ms/step - accuracy: 0.9240 - loss: 0.4342
25/48 āāāāāāāāāāāāāāāāāāāā 3s 152ms/step - accuracy: 0.9241 - loss: 0.4337
26/48 āāāāāāāāāāāāāāāāāāāā 3s 152ms/step - accuracy: 0.9242 - loss: 0.4333
27/48 āāāāāāāāāāāāāāāāāāāā 3s 152ms/step - accuracy: 0.9241 - loss: 0.4328
28/48 āāāāāāāāāāāāāāāāāāāā 3s 152ms/step - accuracy: 0.9242 - loss: 0.4321
29/48 āāāāāāāāāāāāāāāāāāāā 2s 152ms/step - accuracy: 0.9242 - loss: 0.4314
30/48 āāāāāāāāāāāāāāāāāāāā 2s 152ms/step - accuracy: 0.9243 - loss: 0.4308
31/48 āāāāāāāāāāāāāāāāāāāā 2s 152ms/step - accuracy: 0.9243 - loss: 0.4302
32/48 āāāāāāāāāāāāāāāāāāāā 2s 151ms/step - accuracy: 0.9244 - loss: 0.4295
33/48 āāāāāāāāāāāāāāāāāāāā 2s 151ms/step - accuracy: 0.9245 - loss: 0.4289
34/48 āāāāāāāāāāāāāāāāāāāā 2s 151ms/step - accuracy: 0.9246 - loss: 0.4282
35/48 āāāāāāāāāāāāāāāāāāāā 1s 151ms/step - accuracy: 0.9246 - loss: 0.4277
36/48 āāāāāāāāāāāāāāāāāāāā 1s 151ms/step - accuracy: 0.9246 - loss: 0.4272
37/48 āāāāāāāāāāāāāāāāāāāā 1s 151ms/step - accuracy: 0.9245 - loss: 0.4267
38/48 āāāāāāāāāāāāāāāāāāāā 1s 151ms/step - accuracy: 0.9246 - loss: 0.4262
39/48 āāāāāāāāāāāāāāāāāāāā 1s 151ms/step - accuracy: 0.9245 - loss: 0.4257
40/48 āāāāāāāāāāāāāāāāāāāā 1s 151ms/step - accuracy: 0.9245 - loss: 0.4252
41/48 āāāāāāāāāāāāāāāāāāāā 1s 151ms/step - accuracy: 0.9245 - loss: 0.4247
42/48 āāāāāāāāāāāāāāāāāāāā 0s 151ms/step - accuracy: 0.9245 - loss: 0.4243
43/48 āāāāāāāāāāāāāāāāāāāā 0s 151ms/step - accuracy: 0.9245 - loss: 0.4238
44/48 āāāāāāāāāāāāāāāāāāāā 0s 151ms/step - accuracy: 0.9245 - loss: 0.4233
45/48 āāāāāāāāāāāāāāāāāāāā 0s 151ms/step - accuracy: 0.9245 - loss: 0.4228
46/48 āāāāāāāāāāāāāāāāāāāā 0s 151ms/step - accuracy: 0.9244 - loss: 0.4224
47/48 āāāāāāāāāāāāāāāāāāāā 0s 151ms/step - accuracy: 0.9244 - loss: 0.4219
48/48 āāāāāāāāāāāāāāāāāāāā 0s 150ms/step - accuracy: 0.9244 - loss: 0.4215
48/48 āāāāāāāāāāāāāāāāāāāā 8s 158ms/step - accuracy: 0.9243 - loss: 0.4210 - val_accuracy: 0.7081 - val_loss: 0.5573
1/16 āāāāāāāāāāāāāāāāāāāā 1s 91ms/step
4/16 āāāāāāāāāāāāāāāāāāāā 0s 22ms/step
7/16 āāāāāāāāāāāāāāāāāāāā 0s 22ms/step
10/16 āāāāāāāāāāāāāāāāāāāā 0s 22ms/step
13/16 āāāāāāāāāāāāāāāāāāāā 0s 22ms/step
16/16 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step
16/16 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step
Accuracy: 0.7080867850098619 Precision: 0.7375 Recall: 0.6755725190839694
Epoch 1/3
1/140 āāāāāāāāāāāāāāāāāāāā 2:05 906ms/step - accuracy: 0.3750 - loss: 0.7149
2/140 āāāāāāāāāāāāāāāāāāāā 12s 94ms/step - accuracy: 0.4062 - loss: 0.7084
3/140 āāāāāāāāāāāāāāāāāāāā 12s 94ms/step - accuracy: 0.3993 - loss: 0.7115
4/140 āāāāāāāāāāāāāāāāāāāā 12s 94ms/step - accuracy: 0.3971 - loss: 0.7139
5/140 āāāāāāāāāāāāāāāāāāāā 12s 95ms/step - accuracy: 0.3977 - loss: 0.7144
6/140 āāāāāāāāāāāāāāāāāāāā 12s 94ms/step - accuracy: 0.3965 - loss: 0.7145
7/140 āāāāāāāāāāāāāāāāāāāā 12s 94ms/step - accuracy: 0.3966 - loss: 0.7141
8/140 āāāāāāāāāāāāāāāāāāāā 12s 94ms/step - accuracy: 0.3993 - loss: 0.7136
9/140 āāāāāāāāāāāāāāāāāāāā 12s 93ms/step - accuracy: 0.4032 - loss: 0.7132
10/140 āāāāāāāāāāāāāāāāāāāā 12s 93ms/step - accuracy: 0.4075 - loss: 0.7124
11/140 āāāāāāāāāāāāāāāāāāāā 12s 93ms/step - accuracy: 0.4121 - loss: 0.7116
12/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.4159 - loss: 0.7109
13/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.4198 - loss: 0.7102
14/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.4230 - loss: 0.7095
15/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.4260 - loss: 0.7089
16/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.4285 - loss: 0.7084
17/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.4304 - loss: 0.7079
18/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.4321 - loss: 0.7075
19/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.4340 - loss: 0.7071
20/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.4359 - loss: 0.7067
21/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.4376 - loss: 0.7064
22/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.4394 - loss: 0.7060
23/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.4409 - loss: 0.7057
24/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.4422 - loss: 0.7055
25/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.4433 - loss: 0.7052
26/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.4446 - loss: 0.7049
27/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.4459 - loss: 0.7047
28/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.4470 - loss: 0.7044
29/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.4481 - loss: 0.7042
30/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.4491 - loss: 0.7040
31/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.4503 - loss: 0.7037
32/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.4514 - loss: 0.7035
33/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.4526 - loss: 0.7033
34/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.4537 - loss: 0.7031
35/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.4549 - loss: 0.7029
36/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.4560 - loss: 0.7027
37/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.4570 - loss: 0.7025
38/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.4580 - loss: 0.7023
39/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.4590 - loss: 0.7022
40/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.4600 - loss: 0.7020
41/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.4610 - loss: 0.7018
42/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.4621 - loss: 0.7016
43/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.4631 - loss: 0.7015
44/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.4640 - loss: 0.7013
45/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.4650 - loss: 0.7012
46/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.4659 - loss: 0.7010
47/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.4668 - loss: 0.7009
48/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.4676 - loss: 0.7007
49/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.4684 - loss: 0.7005
50/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.4693 - loss: 0.7004
51/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.4702 - loss: 0.7002
52/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.4710 - loss: 0.7001
53/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.4718 - loss: 0.7000
54/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.4726 - loss: 0.6998
55/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.4733 - loss: 0.6997
56/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.4740 - loss: 0.6996
57/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.4746 - loss: 0.6995
58/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.4752 - loss: 0.6993
59/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.4758 - loss: 0.6992
60/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.4764 - loss: 0.6991
61/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.4769 - loss: 0.6990
62/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.4774 - loss: 0.6989
63/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.4780 - loss: 0.6988
64/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.4785 - loss: 0.6987
65/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.4790 - loss: 0.6986
66/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.4795 - loss: 0.6985
67/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.4800 - loss: 0.6984
68/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.4805 - loss: 0.6983
69/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.4810 - loss: 0.6982
70/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.4815 - loss: 0.6981
71/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.4820 - loss: 0.6980
72/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.4825 - loss: 0.6979
73/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.4830 - loss: 0.6978
74/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.4835 - loss: 0.6977
75/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.4839 - loss: 0.6976
76/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.4844 - loss: 0.6975
77/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.4848 - loss: 0.6975
78/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.4853 - loss: 0.6974
79/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.4857 - loss: 0.6973
80/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.4861 - loss: 0.6972
81/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.4865 - loss: 0.6971
82/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.4869 - loss: 0.6970
83/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.4873 - loss: 0.6970
84/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.4877 - loss: 0.6969
85/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.4880 - loss: 0.6968
86/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.4884 - loss: 0.6967
87/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.4887 - loss: 0.6967
88/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.4890 - loss: 0.6966
89/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.4894 - loss: 0.6965
90/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.4897 - loss: 0.6965
91/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.4900 - loss: 0.6964
92/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.4903 - loss: 0.6963
93/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.4907 - loss: 0.6962
94/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.4910 - loss: 0.6962
95/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.4913 - loss: 0.6961
96/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.4916 - loss: 0.6960
97/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.4919 - loss: 0.6960
98/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.4922 - loss: 0.6959
99/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.4926 - loss: 0.6958
100/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.4929 - loss: 0.6957
101/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.4932 - loss: 0.6957
102/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.4935 - loss: 0.6956
103/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.4938 - loss: 0.6956
104/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.4941 - loss: 0.6955
105/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.4944 - loss: 0.6954
106/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.4946 - loss: 0.6954
107/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.4949 - loss: 0.6953
108/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.4952 - loss: 0.6952
109/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.4955 - loss: 0.6952
110/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.4957 - loss: 0.6951
111/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.4959 - loss: 0.6951
112/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.4962 - loss: 0.6950
113/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.4964 - loss: 0.6950
114/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.4967 - loss: 0.6949
115/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.4970 - loss: 0.6949
116/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.4972 - loss: 0.6948
117/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.4975 - loss: 0.6947
118/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.4978 - loss: 0.6947
119/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.4980 - loss: 0.6946
120/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.4983 - loss: 0.6946
121/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.4986 - loss: 0.6945
122/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.4989 - loss: 0.6945
123/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.4991 - loss: 0.6944
124/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.4994 - loss: 0.6943
125/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.4997 - loss: 0.6943
126/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.4999 - loss: 0.6942
127/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.5002 - loss: 0.6942
128/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.5005 - loss: 0.6941
129/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.5007 - loss: 0.6941
130/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.5010 - loss: 0.6940
131/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.5012 - loss: 0.6939
132/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.5015 - loss: 0.6939
133/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.5017 - loss: 0.6938
134/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.5020 - loss: 0.6938
135/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.5022 - loss: 0.6937
136/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.5025 - loss: 0.6937
137/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.5027 - loss: 0.6936
138/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.5030 - loss: 0.6935
139/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.5032 - loss: 0.6935
140/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.5034 - loss: 0.6934
140/140 āāāāāāāāāāāāāāāāāāāā 15s 99ms/step - accuracy: 0.5037 - loss: 0.6934 - val_accuracy: 0.6152 - val_loss: 0.6668
Epoch 2/3
1/140 āāāāāāāāāāāāāāāāāāāā 14s 101ms/step - accuracy: 0.7500 - loss: 0.6164
2/140 āāāāāāāāāāāāāāāāāāāā 12s 91ms/step - accuracy: 0.7734 - loss: 0.6114
3/140 āāāāāāāāāāāāāāāāāāāā 12s 92ms/step - accuracy: 0.7899 - loss: 0.6063
4/140 āāāāāāāāāāāāāāāāāāāā 12s 91ms/step - accuracy: 0.7975 - loss: 0.6056
5/140 āāāāāāāāāāāāāāāāāāāā 12s 92ms/step - accuracy: 0.8055 - loss: 0.6042
6/140 āāāāāāāāāāāāāāāāāāāā 12s 92ms/step - accuracy: 0.8136 - loss: 0.6030
7/140 āāāāāāāāāāāāāāāāāāāā 12s 92ms/step - accuracy: 0.8160 - loss: 0.6027
8/140 āāāāāāāāāāāāāāāāāāāā 12s 93ms/step - accuracy: 0.8166 - loss: 0.6027
9/140 āāāāāāāāāāāāāāāāāāāā 12s 93ms/step - accuracy: 0.8180 - loss: 0.6026
10/140 āāāāāāāāāāāāāāāāāāāā 12s 93ms/step - accuracy: 0.8203 - loss: 0.6027
11/140 āāāāāāāāāāāāāāāāāāāā 11s 92ms/step - accuracy: 0.8224 - loss: 0.6029
12/140 āāāāāāāāāāāāāāāāāāāā 11s 92ms/step - accuracy: 0.8244 - loss: 0.6030
13/140 āāāāāāāāāāāāāāāāāāāā 11s 92ms/step - accuracy: 0.8259 - loss: 0.6031
14/140 āāāāāāāāāāāāāāāāāāāā 11s 92ms/step - accuracy: 0.8270 - loss: 0.6032
15/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.8276 - loss: 0.6032
16/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.8281 - loss: 0.6033
17/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.8288 - loss: 0.6032
18/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.8295 - loss: 0.6031
19/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.8304 - loss: 0.6030
20/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.8313 - loss: 0.6028
21/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.8321 - loss: 0.6026
22/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.8329 - loss: 0.6023
23/140 āāāāāāāāāāāāāāāāāāāā 10s 92ms/step - accuracy: 0.8338 - loss: 0.6020
24/140 āāāāāāāāāāāāāāāāāāāā 10s 92ms/step - accuracy: 0.8346 - loss: 0.6017
25/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.8353 - loss: 0.6014
26/140 āāāāāāāāāāāāāāāāāāāā 10s 92ms/step - accuracy: 0.8356 - loss: 0.6012
27/140 āāāāāāāāāāāāāāāāāāāā 10s 92ms/step - accuracy: 0.8361 - loss: 0.6009
28/140 āāāāāāāāāāāāāāāāāāāā 10s 92ms/step - accuracy: 0.8366 - loss: 0.6006
29/140 āāāāāāāāāāāāāāāāāāāā 10s 92ms/step - accuracy: 0.8368 - loss: 0.6003
30/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.8371 - loss: 0.6000
31/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.8375 - loss: 0.5997
32/140 āāāāāāāāāāāāāāāāāāāā 9s 92ms/step - accuracy: 0.8379 - loss: 0.5993
33/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.8383 - loss: 0.5990
34/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.8387 - loss: 0.5986
35/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.8390 - loss: 0.5983
36/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.8394 - loss: 0.5979
37/140 āāāāāāāāāāāāāāāāāāāā 9s 92ms/step - accuracy: 0.8398 - loss: 0.5975
38/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.8403 - loss: 0.5972
39/140 āāāāāāāāāāāāāāāāāāāā 9s 92ms/step - accuracy: 0.8406 - loss: 0.5969
40/140 āāāāāāāāāāāāāāāāāāāā 9s 92ms/step - accuracy: 0.8410 - loss: 0.5965
41/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.8413 - loss: 0.5962
42/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.8416 - loss: 0.5958
43/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.8419 - loss: 0.5955
44/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.8422 - loss: 0.5951
45/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.8425 - loss: 0.5948
46/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.8427 - loss: 0.5944
47/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.8428 - loss: 0.5941
48/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.8430 - loss: 0.5938
49/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.8431 - loss: 0.5934
50/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.8432 - loss: 0.5931
51/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.8432 - loss: 0.5928
52/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.8433 - loss: 0.5925
53/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.8434 - loss: 0.5922
54/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.8435 - loss: 0.5918
55/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.8435 - loss: 0.5915
56/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.8435 - loss: 0.5912
57/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.8436 - loss: 0.5908
58/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.8436 - loss: 0.5905
59/140 āāāāāāāāāāāāāāāāāāāā 7s 92ms/step - accuracy: 0.8437 - loss: 0.5902
60/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.8437 - loss: 0.5898
61/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.8437 - loss: 0.5895
62/140 āāāāāāāāāāāāāāāāāāāā 7s 92ms/step - accuracy: 0.8437 - loss: 0.5892
63/140 āāāāāāāāāāāāāāāāāāāā 7s 92ms/step - accuracy: 0.8437 - loss: 0.5888
64/140 āāāāāāāāāāāāāāāāāāāā 7s 92ms/step - accuracy: 0.8437 - loss: 0.5885
65/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.8437 - loss: 0.5882
66/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.8437 - loss: 0.5878
67/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.8437 - loss: 0.5875
68/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.8438 - loss: 0.5872
69/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.8438 - loss: 0.5869
70/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.8438 - loss: 0.5865
71/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.8438 - loss: 0.5862
72/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.8438 - loss: 0.5859
73/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.8438 - loss: 0.5855
74/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.8438 - loss: 0.5852
75/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.8438 - loss: 0.5848
76/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.8438 - loss: 0.5844
77/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.8438 - loss: 0.5841
78/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.8438 - loss: 0.5837
79/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.8438 - loss: 0.5834
80/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.8438 - loss: 0.5830
81/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.8439 - loss: 0.5827
82/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.8439 - loss: 0.5823
83/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.8439 - loss: 0.5820
84/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.8438 - loss: 0.5816
85/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.8439 - loss: 0.5813
86/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.8439 - loss: 0.5809
87/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.8438 - loss: 0.5806
88/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.8438 - loss: 0.5803
89/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.8438 - loss: 0.5799
90/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.8438 - loss: 0.5796
91/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.8438 - loss: 0.5792
92/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.8438 - loss: 0.5789
93/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.8438 - loss: 0.5785
94/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.8438 - loss: 0.5782
95/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.8438 - loss: 0.5779
96/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.8438 - loss: 0.5775
97/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.8437 - loss: 0.5772
98/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.8437 - loss: 0.5769
99/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.8437 - loss: 0.5765
100/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.8437 - loss: 0.5762
101/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.8436 - loss: 0.5758
102/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.8436 - loss: 0.5755
103/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.8436 - loss: 0.5751
104/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.8436 - loss: 0.5748
105/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.8436 - loss: 0.5745
106/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.8435 - loss: 0.5741
107/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.8435 - loss: 0.5738
108/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.8434 - loss: 0.5735
109/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.8434 - loss: 0.5732
110/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.8434 - loss: 0.5728
111/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.8434 - loss: 0.5725
112/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.8433 - loss: 0.5722
113/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.8433 - loss: 0.5719
114/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.8433 - loss: 0.5716
115/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.8432 - loss: 0.5713
116/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.8432 - loss: 0.5709
117/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.8432 - loss: 0.5706
118/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.8431 - loss: 0.5703
119/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.8431 - loss: 0.5700
120/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.8431 - loss: 0.5697
121/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.8430 - loss: 0.5694
122/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.8430 - loss: 0.5691
123/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.8429 - loss: 0.5688
124/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.8429 - loss: 0.5685
125/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.8429 - loss: 0.5681
126/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.8429 - loss: 0.5678
127/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.8429 - loss: 0.5675
128/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.8429 - loss: 0.5672
129/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.8429 - loss: 0.5668
130/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.8429 - loss: 0.5665
131/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.8429 - loss: 0.5662
132/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.8429 - loss: 0.5659
133/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.8429 - loss: 0.5655
134/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.8429 - loss: 0.5652
135/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.8429 - loss: 0.5648
136/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.8429 - loss: 0.5645
137/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.8429 - loss: 0.5642
138/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.8429 - loss: 0.5638
139/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.8429 - loss: 0.5635
140/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.8429 - loss: 0.5631
140/140 āāāāāāāāāāāāāāāāāāāā 14s 98ms/step - accuracy: 0.8429 - loss: 0.5628 - val_accuracy: 0.7170 - val_loss: 0.5472
Epoch 3/3
1/140 āāāāāāāāāāāāāāāāāāāā 14s 104ms/step - accuracy: 0.8750 - loss: 0.3014
2/140 āāāāāāāāāāāāāāāāāāāā 12s 94ms/step - accuracy: 0.8906 - loss: 0.3083
3/140 āāāāāāāāāāāāāāāāāāāā 12s 95ms/step - accuracy: 0.9028 - loss: 0.3026
4/140 āāāāāāāāāāāāāāāāāāāā 12s 95ms/step - accuracy: 0.9095 - loss: 0.2944
5/140 āāāāāāāāāāāāāāāāāāāā 12s 95ms/step - accuracy: 0.9139 - loss: 0.2912
6/140 āāāāāāāāāāāāāāāāāāāā 12s 95ms/step - accuracy: 0.9169 - loss: 0.2899
7/140 āāāāāāāāāāāāāāāāāāāā 12s 95ms/step - accuracy: 0.9199 - loss: 0.2877
8/140 āāāāāāāāāāāāāāāāāāāā 12s 95ms/step - accuracy: 0.9206 - loss: 0.2861
9/140 āāāāāāāāāāāāāāāāāāāā 12s 94ms/step - accuracy: 0.9194 - loss: 0.2861
10/140 āāāāāāāāāāāāāāāāāāāā 12s 94ms/step - accuracy: 0.9184 - loss: 0.2866
11/140 āāāāāāāāāāāāāāāāāāāā 12s 94ms/step - accuracy: 0.9181 - loss: 0.2864
12/140 āāāāāāāāāāāāāāāāāāāā 12s 94ms/step - accuracy: 0.9179 - loss: 0.2858
13/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.9183 - loss: 0.2851
14/140 āāāāāāāāāāāāāāāāāāāā 11s 94ms/step - accuracy: 0.9191 - loss: 0.2842
15/140 āāāāāāāāāāāāāāāāāāāā 11s 94ms/step - accuracy: 0.9193 - loss: 0.2836
16/140 āāāāāāāāāāāāāāāāāāāā 11s 94ms/step - accuracy: 0.9196 - loss: 0.2829
17/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.9199 - loss: 0.2820
18/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.9200 - loss: 0.2813
19/140 āāāāāāāāāāāāāāāāāāāā 11s 94ms/step - accuracy: 0.9202 - loss: 0.2806
20/140 āāāāāāāāāāāāāāāāāāāā 11s 94ms/step - accuracy: 0.9206 - loss: 0.2799
21/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.9209 - loss: 0.2791
22/140 āāāāāāāāāāāāāāāāāāāā 11s 93ms/step - accuracy: 0.9212 - loss: 0.2783
23/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.9217 - loss: 0.2775
24/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.9221 - loss: 0.2766
25/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.9225 - loss: 0.2758
26/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.9229 - loss: 0.2751
27/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.9232 - loss: 0.2744
28/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.9234 - loss: 0.2738
29/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.9236 - loss: 0.2732
30/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.9239 - loss: 0.2726
31/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.9242 - loss: 0.2720
32/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.9246 - loss: 0.2714
33/140 āāāāāāāāāāāāāāāāāāāā 10s 93ms/step - accuracy: 0.9248 - loss: 0.2708
34/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.9251 - loss: 0.2702
35/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.9254 - loss: 0.2696
36/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.9257 - loss: 0.2691
37/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.9259 - loss: 0.2685
38/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.9261 - loss: 0.2680
39/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.9264 - loss: 0.2675
40/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.9267 - loss: 0.2670
41/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.9269 - loss: 0.2665
42/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.9272 - loss: 0.2661
43/140 āāāāāāāāāāāāāāāāāāāā 9s 93ms/step - accuracy: 0.9275 - loss: 0.2655
44/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.9277 - loss: 0.2651
45/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.9280 - loss: 0.2646
46/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.9282 - loss: 0.2641
47/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.9285 - loss: 0.2636
48/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.9287 - loss: 0.2632
49/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.9289 - loss: 0.2627
50/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.9291 - loss: 0.2623
51/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.9293 - loss: 0.2619
52/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.9294 - loss: 0.2615
53/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.9296 - loss: 0.2611
54/140 āāāāāāāāāāāāāāāāāāāā 8s 93ms/step - accuracy: 0.9299 - loss: 0.2607
55/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.9300 - loss: 0.2602
56/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.9302 - loss: 0.2598
57/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.9304 - loss: 0.2594
58/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.9305 - loss: 0.2590
59/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.9307 - loss: 0.2586
60/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.9308 - loss: 0.2582
61/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.9310 - loss: 0.2578
62/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.9311 - loss: 0.2575
63/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.9313 - loss: 0.2571
64/140 āāāāāāāāāāāāāāāāāāāā 7s 93ms/step - accuracy: 0.9314 - loss: 0.2567
65/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.9315 - loss: 0.2564
66/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.9316 - loss: 0.2560
67/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.9318 - loss: 0.2557
68/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.9319 - loss: 0.2553
69/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.9320 - loss: 0.2550
70/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.9321 - loss: 0.2546
71/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.9322 - loss: 0.2543
72/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.9323 - loss: 0.2540
73/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.9324 - loss: 0.2536
74/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.9326 - loss: 0.2533
75/140 āāāāāāāāāāāāāāāāāāāā 6s 93ms/step - accuracy: 0.9327 - loss: 0.2530
76/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.9328 - loss: 0.2527
77/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.9328 - loss: 0.2524
78/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.9329 - loss: 0.2521
79/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.9330 - loss: 0.2518
80/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.9331 - loss: 0.2515
81/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.9331 - loss: 0.2513
82/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.9332 - loss: 0.2511
83/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.9332 - loss: 0.2508
84/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.9332 - loss: 0.2506
85/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.9333 - loss: 0.2504
86/140 āāāāāāāāāāāāāāāāāāāā 5s 93ms/step - accuracy: 0.9333 - loss: 0.2502
87/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.9333 - loss: 0.2499
88/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.9334 - loss: 0.2497
89/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.9334 - loss: 0.2495
90/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.9334 - loss: 0.2493
91/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.9335 - loss: 0.2491
92/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.9335 - loss: 0.2489
93/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.9335 - loss: 0.2488
94/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.9335 - loss: 0.2486
95/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.9335 - loss: 0.2484
96/140 āāāāāāāāāāāāāāāāāāāā 4s 93ms/step - accuracy: 0.9335 - loss: 0.2482
97/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.9335 - loss: 0.2481
98/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.9335 - loss: 0.2479
99/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.9335 - loss: 0.2477
100/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.9335 - loss: 0.2476
101/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.9335 - loss: 0.2474
102/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.9335 - loss: 0.2472
103/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.9335 - loss: 0.2471
104/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.9335 - loss: 0.2469
105/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.9335 - loss: 0.2467
106/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.9334 - loss: 0.2466
107/140 āāāāāāāāāāāāāāāāāāāā 3s 93ms/step - accuracy: 0.9334 - loss: 0.2464
108/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.9334 - loss: 0.2463
109/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.9334 - loss: 0.2461
110/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.9334 - loss: 0.2459
111/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.9334 - loss: 0.2457
112/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.9334 - loss: 0.2456
113/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.9334 - loss: 0.2454
114/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.9335 - loss: 0.2452
115/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.9335 - loss: 0.2450
116/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.9335 - loss: 0.2448
117/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.9335 - loss: 0.2447
118/140 āāāāāāāāāāāāāāāāāāāā 2s 93ms/step - accuracy: 0.9335 - loss: 0.2445
119/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.9336 - loss: 0.2443
120/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.9336 - loss: 0.2441
121/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.9336 - loss: 0.2439
122/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.9336 - loss: 0.2437
123/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.9337 - loss: 0.2435
124/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.9337 - loss: 0.2433
125/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.9337 - loss: 0.2431
126/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.9337 - loss: 0.2430
127/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.9338 - loss: 0.2428
128/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.9338 - loss: 0.2426
129/140 āāāāāāāāāāāāāāāāāāāā 1s 93ms/step - accuracy: 0.9338 - loss: 0.2424
130/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.9339 - loss: 0.2422
131/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.9339 - loss: 0.2420
132/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.9339 - loss: 0.2418
133/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.9339 - loss: 0.2416
134/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.9340 - loss: 0.2415
135/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.9340 - loss: 0.2413
136/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.9340 - loss: 0.2411
137/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.9340 - loss: 0.2409
138/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.9341 - loss: 0.2407
139/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.9341 - loss: 0.2405
140/140 āāāāāāāāāāāāāāāāāāāā 0s 93ms/step - accuracy: 0.9341 - loss: 0.2404
140/140 āāāāāāāāāāāāāāāāāāāā 14s 98ms/step - accuracy: 0.9341 - loss: 0.2402 - val_accuracy: 0.6685 - val_loss: 0.6943
1/47 āāāāāāāāāāāāāāāāāāāā 3s 85ms/step
4/47 āāāāāāāāāāāāāāāāāāāā 0s 17ms/step
8/47 āāāāāāāāāāāāāāāāāāāā 0s 16ms/step
12/47 āāāāāāāāāāāāāāāāāāāā 0s 16ms/step
16/47 āāāāāāāāāāāāāāāāāāāā 0s 16ms/step
20/47 āāāāāāāāāāāāāāāāāāāā 0s 16ms/step
24/47 āāāāāāāāāāāāāāāāāāāā 0s 16ms/step
28/47 āāāāāāāāāāāāāāāāāāāā 0s 16ms/step
32/47 āāāāāāāāāāāāāāāāāāāā 0s 16ms/step
36/47 āāāāāāāāāāāāāāāāāāāā 0s 16ms/step
40/47 āāāāāāāāāāāāāāāāāāāā 0s 16ms/step
44/47 āāāāāāāāāāāāāāāāāāāā 0s 16ms/step
47/47 āāāāāāāāāāāāāāāāāāāā 0s 17ms/step
47/47 āāāāāāāāāāāāāāāāāāāā 1s 17ms/step
Accuracy: 0.6684636118598383 Precision: 0.6083916083916084 Recall: 0.9405405405405406 Average accuracy: 0.7653718745110875 Average precision: 0.7498315276345204 Average recall: 0.8338914050264655
InĀ [28]:
from tensorflow.keras.layers import LSTM
InĀ [29]:
accuracy = []
precision = []
recall = []
for dataset in chosen_datasets:
df = pd.read_csv(os.path.join("datasets", dataset))
X_train, X_test, y_train, y_test = train_test_split(df["text"].values, df["label"].values, test_size=0.25, random_state=1)
tokenizer = Tokenizer()
tokenizer.fit_on_texts(X_train)
sequences = tokenizer.texts_to_sequences(X_train)
max_length = max([len(seq) for seq in sequences])
padded_sequences = pad_sequences(sequences, maxlen=max_length)
new_sequences = tokenizer.texts_to_sequences(X_test)
new_padded_sequences = pad_sequences(new_sequences, maxlen=max_length)
model = Sequential()
model.add(Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=32))
model.add(LSTM(32))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
epochs = 5
history = model.fit(padded_sequences, y_train, validation_data=(new_padded_sequences, y_test), epochs=epochs)
print_metrics(history, epochs=epochs)
predictions = model.predict(new_padded_sequences)
correct = 0
predicted_labels = []
for i in range(len(predictions)):
prediction_label = 1 if predictions[i] > 0.5 else 0
predicted_labels.append(prediction_label)
if y_test[i] == prediction_label:
correct += 1
print_conf_matrix(y_test, predicted_labels)
accuracy.append(correct / len(predictions))
print(f"Accuracy: {correct / len(predictions)}")
print(f"Precision: {precision_score(y_test, predicted_labels)}")
precision.append(precision_score(y_test, predicted_labels))
print(f"Recall: {recall_score(y_test, predicted_labels)}")
recall.append(recall_score(y_test, predicted_labels))
print(f"Average accuracy: {np.average(accuracy)}")
print(f"Average precision: {np.average(precision)}")
print(f"Average recall: {np.average(recall)}")
Epoch 1/5
1/60 āāāāāāāāāāāāāāāāāāāā 1:20 1s/step - accuracy: 0.3438 - loss: 0.6944
2/60 āāāāāāāāāāāāāāāāāāāā 12s 211ms/step - accuracy: 0.3750 - loss: 0.6945
3/60 āāāāāāāāāāāāāāāāāāāā 11s 210ms/step - accuracy: 0.3819 - loss: 0.6946
4/60 āāāāāāāāāāāāāāāāāāāā 11s 211ms/step - accuracy: 0.3997 - loss: 0.6945
5/60 āāāāāāāāāāāāāāāāāāāā 11s 208ms/step - accuracy: 0.4110 - loss: 0.6944
6/60 āāāāāāāāāāāāāāāāāāāā 11s 208ms/step - accuracy: 0.4146 - loss: 0.6944
7/60 āāāāāāāāāāāāāāāāāāāā 10s 207ms/step - accuracy: 0.4210 - loss: 0.6943
8/60 āāāāāāāāāāāāāāāāāāāā 10s 205ms/step - accuracy: 0.4260 - loss: 0.6943
9/60 āāāāāāāāāāāāāāāāāāāā 10s 205ms/step - accuracy: 0.4327 - loss: 0.6942
10/60 āāāāāāāāāāāāāāāāāāāā 10s 205ms/step - accuracy: 0.4394 - loss: 0.6941
11/60 āāāāāāāāāāāāāāāāāāāā 10s 205ms/step - accuracy: 0.4465 - loss: 0.6940
12/60 āāāāāāāāāāāāāāāāāāāā 9s 204ms/step - accuracy: 0.4531 - loss: 0.6939
13/60 āāāāāāāāāāāāāāāāāāāā 9s 204ms/step - accuracy: 0.4595 - loss: 0.6938
14/60 āāāāāāāāāāāāāāāāāāāā 9s 204ms/step - accuracy: 0.4659 - loss: 0.6937
15/60 āāāāāāāāāāāāāāāāāāāā 9s 204ms/step - accuracy: 0.4725 - loss: 0.6936
16/60 āāāāāāāāāāāāāāāāāāāā 8s 204ms/step - accuracy: 0.4796 - loss: 0.6935
17/60 āāāāāāāāāāāāāāāāāāāā 8s 204ms/step - accuracy: 0.4856 - loss: 0.6934
18/60 āāāāāāāāāāāāāāāāāāāā 8s 205ms/step - accuracy: 0.4911 - loss: 0.6933
19/60 āāāāāāāāāāāāāāāāāāāā 8s 205ms/step - accuracy: 0.4961 - loss: 0.6933
20/60 āāāāāāāāāāāāāāāāāāāā 8s 205ms/step - accuracy: 0.5007 - loss: 0.6932
21/60 āāāāāāāāāāāāāāāāāāāā 7s 205ms/step - accuracy: 0.5052 - loss: 0.6931
22/60 āāāāāāāāāāāāāāāāāāāā 7s 205ms/step - accuracy: 0.5097 - loss: 0.6930
23/60 āāāāāāāāāāāāāāāāāāāā 7s 205ms/step - accuracy: 0.5142 - loss: 0.6930
24/60 āāāāāāāāāāāāāāāāāāāā 7s 205ms/step - accuracy: 0.5188 - loss: 0.6929
25/60 āāāāāāāāāāāāāāāāāāāā 7s 205ms/step - accuracy: 0.5232 - loss: 0.6928
26/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.5273 - loss: 0.6927
27/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.5312 - loss: 0.6926
28/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.5348 - loss: 0.6926
29/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.5382 - loss: 0.6925
30/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.5413 - loss: 0.6924
31/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.5444 - loss: 0.6923
32/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.5476 - loss: 0.6922
33/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.5506 - loss: 0.6921
34/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.5536 - loss: 0.6920
35/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.5564 - loss: 0.6919
36/60 āāāāāāāāāāāāāāāāāāāā 4s 205ms/step - accuracy: 0.5592 - loss: 0.6917
37/60 āāāāāāāāāāāāāāāāāāāā 4s 205ms/step - accuracy: 0.5618 - loss: 0.6916
38/60 āāāāāāāāāāāāāāāāāāāā 4s 205ms/step - accuracy: 0.5642 - loss: 0.6915
39/60 āāāāāāāāāāāāāāāāāāāā 4s 205ms/step - accuracy: 0.5666 - loss: 0.6914
40/60 āāāāāāāāāāāāāāāāāāāā 4s 204ms/step - accuracy: 0.5689 - loss: 0.6912
41/60 āāāāāāāāāāāāāāāāāāāā 3s 204ms/step - accuracy: 0.5713 - loss: 0.6911
42/60 āāāāāāāāāāāāāāāāāāāā 3s 204ms/step - accuracy: 0.5735 - loss: 0.6909
43/60 āāāāāāāāāāāāāāāāāāāā 3s 204ms/step - accuracy: 0.5755 - loss: 0.6907
44/60 āāāāāāāāāāāāāāāāāāāā 3s 204ms/step - accuracy: 0.5776 - loss: 0.6906
45/60 āāāāāāāāāāāāāāāāāāāā 3s 204ms/step - accuracy: 0.5795 - loss: 0.6903
46/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.5813 - loss: 0.6902
47/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.5830 - loss: 0.6900
48/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.5846 - loss: 0.6898
49/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.5863 - loss: 0.6896
50/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.5879 - loss: 0.6894
51/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.5895 - loss: 0.6891
52/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.5910 - loss: 0.6889
53/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.5926 - loss: 0.6886
54/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.5941 - loss: 0.6883
55/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.5957 - loss: 0.6880
56/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.5972 - loss: 0.6877
57/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.5988 - loss: 0.6873
58/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.6003 - loss: 0.6869
59/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.6018 - loss: 0.6865
60/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.6032 - loss: 0.6861
60/60 āāāāāāāāāāāāāāāāāāāā 14s 222ms/step - accuracy: 0.6046 - loss: 0.6858 - val_accuracy: 0.5000 - val_loss: 0.8668
Epoch 2/5
1/60 āāāāāāāāāāāāāāāāāāāā 12s 208ms/step - accuracy: 0.5312 - loss: 0.8405
2/60 āāāāāāāāāāāāāāāāāāāā 11s 200ms/step - accuracy: 0.5234 - loss: 0.8308
3/60 āāāāāāāāāāāāāāāāāāāā 11s 204ms/step - accuracy: 0.5122 - loss: 0.8319
4/60 āāāāāāāāāāāāāāāāāāāā 11s 203ms/step - accuracy: 0.5013 - loss: 0.8264
5/60 āāāāāāāāāāāāāāāāāāāā 11s 205ms/step - accuracy: 0.5123 - loss: 0.8080
6/60 āāāāāāāāāāāāāāāāāāāā 10s 204ms/step - accuracy: 0.5250 - loss: 0.7899
7/60 āāāāāāāāāāāāāāāāāāāā 10s 204ms/step - accuracy: 0.5374 - loss: 0.7736
8/60 āāāāāāāāāāāāāāāāāāāā 10s 204ms/step - accuracy: 0.5503 - loss: 0.7578
9/60 āāāāāāāāāāāāāāāāāāāā 10s 204ms/step - accuracy: 0.5628 - loss: 0.7440
10/60 āāāāāāāāāāāāāāāāāāāā 10s 204ms/step - accuracy: 0.5747 - loss: 0.7315
11/60 āāāāāāāāāāāāāāāāāāāā 9s 203ms/step - accuracy: 0.5857 - loss: 0.7204
12/60 āāāāāāāāāāāāāāāāāāāā 9s 203ms/step - accuracy: 0.5957 - loss: 0.7103
13/60 āāāāāāāāāāāāāāāāāāāā 9s 203ms/step - accuracy: 0.6054 - loss: 0.7013
14/60 āāāāāāāāāāāāāāāāāāāā 9s 203ms/step - accuracy: 0.6147 - loss: 0.6928
15/60 āāāāāāāāāāāāāāāāāāāā 9s 202ms/step - accuracy: 0.6231 - loss: 0.6850
16/60 āāāāāāāāāāāāāāāāāāāā 8s 202ms/step - accuracy: 0.6306 - loss: 0.6778
17/60 āāāāāāāāāāāāāāāāāāāā 8s 203ms/step - accuracy: 0.6370 - loss: 0.6716
18/60 āāāāāāāāāāāāāāāāāāāā 8s 204ms/step - accuracy: 0.6432 - loss: 0.6657
19/60 āāāāāāāāāāāāāāāāāāāā 8s 204ms/step - accuracy: 0.6491 - loss: 0.6602
20/60 āāāāāāāāāāāāāāāāāāāā 8s 204ms/step - accuracy: 0.6548 - loss: 0.6549
21/60 āāāāāāāāāāāāāāāāāāāā 7s 204ms/step - accuracy: 0.6603 - loss: 0.6499
22/60 āāāāāāāāāāāāāāāāāāāā 7s 204ms/step - accuracy: 0.6658 - loss: 0.6450
23/60 āāāāāāāāāāāāāāāāāāāā 7s 204ms/step - accuracy: 0.6710 - loss: 0.6403
24/60 āāāāāāāāāāāāāāāāāāāā 7s 204ms/step - accuracy: 0.6759 - loss: 0.6359
25/60 āāāāāāāāāāāāāāāāāāāā 7s 204ms/step - accuracy: 0.6805 - loss: 0.6317
26/60 āāāāāāāāāāāāāāāāāāāā 6s 204ms/step - accuracy: 0.6849 - loss: 0.6276
27/60 āāāāāāāāāāāāāāāāāāāā 6s 204ms/step - accuracy: 0.6891 - loss: 0.6236
28/60 āāāāāāāāāāāāāāāāāāāā 6s 204ms/step - accuracy: 0.6931 - loss: 0.6199
29/60 āāāāāāāāāāāāāāāāāāāā 6s 204ms/step - accuracy: 0.6970 - loss: 0.6162
30/60 āāāāāāāāāāāāāāāāāāāā 6s 204ms/step - accuracy: 0.7007 - loss: 0.6126
31/60 āāāāāāāāāāāāāāāāāāāā 5s 204ms/step - accuracy: 0.7044 - loss: 0.6091
32/60 āāāāāāāāāāāāāāāāāāāā 5s 204ms/step - accuracy: 0.7078 - loss: 0.6056
33/60 āāāāāāāāāāāāāāāāāāāā 5s 204ms/step - accuracy: 0.7111 - loss: 0.6023
34/60 āāāāāāāāāāāāāāāāāāāā 5s 204ms/step - accuracy: 0.7141 - loss: 0.5992
35/60 āāāāāāāāāāāāāāāāāāāā 5s 204ms/step - accuracy: 0.7170 - loss: 0.5962
36/60 āāāāāāāāāāāāāāāāāāāā 4s 204ms/step - accuracy: 0.7198 - loss: 0.5933
37/60 āāāāāāāāāāāāāāāāāāāā 4s 204ms/step - accuracy: 0.7225 - loss: 0.5904
38/60 āāāāāāāāāāāāāāāāāāāā 4s 204ms/step - accuracy: 0.7250 - loss: 0.5876
39/60 āāāāāāāāāāāāāāāāāāāā 4s 204ms/step - accuracy: 0.7275 - loss: 0.5848
40/60 āāāāāāāāāāāāāāāāāāāā 4s 204ms/step - accuracy: 0.7300 - loss: 0.5821
41/60 āāāāāāāāāāāāāāāāāāāā 3s 204ms/step - accuracy: 0.7323 - loss: 0.5793
42/60 āāāāāāāāāāāāāāāāāāāā 3s 204ms/step - accuracy: 0.7346 - loss: 0.5767
43/60 āāāāāāāāāāāāāāāāāāāā 3s 204ms/step - accuracy: 0.7369 - loss: 0.5740
44/60 āāāāāāāāāāāāāāāāāāāā 3s 204ms/step - accuracy: 0.7391 - loss: 0.5714
45/60 āāāāāāāāāāāāāāāāāāāā 3s 204ms/step - accuracy: 0.7412 - loss: 0.5689
46/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.7432 - loss: 0.5664
47/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.7451 - loss: 0.5641
48/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.7470 - loss: 0.5617
49/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.7489 - loss: 0.5594
50/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.7506 - loss: 0.5572
51/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.7523 - loss: 0.5550
52/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.7540 - loss: 0.5528
53/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.7556 - loss: 0.5507
54/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.7571 - loss: 0.5486
55/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.7586 - loss: 0.5466
56/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.7601 - loss: 0.5445
57/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.7616 - loss: 0.5425
58/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.7630 - loss: 0.5405
59/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.7644 - loss: 0.5386
60/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.7657 - loss: 0.5368
60/60 āāāāāāāāāāāāāāāāāāāā 13s 220ms/step - accuracy: 0.7669 - loss: 0.5350 - val_accuracy: 0.8817 - val_loss: 0.3046
Epoch 3/5
1/60 āāāāāāāāāāāāāāāāāāāā 12s 213ms/step - accuracy: 0.9062 - loss: 0.2917
2/60 āāāāāāāāāāāāāāāāāāāā 11s 193ms/step - accuracy: 0.9062 - loss: 0.2916
3/60 āāāāāāāāāāāāāāāāāāāā 11s 201ms/step - accuracy: 0.9167 - loss: 0.2805
4/60 āāāāāāāāāāāāāāāāāāāā 11s 200ms/step - accuracy: 0.9199 - loss: 0.2766
5/60 āāāāāāāāāāāāāāāāāāāā 11s 201ms/step - accuracy: 0.9222 - loss: 0.2739
6/60 āāāāāāāāāāāāāāāāāāāā 10s 202ms/step - accuracy: 0.9247 - loss: 0.2720
7/60 āāāāāāāāāāāāāāāāāāāā 10s 204ms/step - accuracy: 0.9253 - loss: 0.2718
8/60 āāāāāāāāāāāāāāāāāāāā 10s 204ms/step - accuracy: 0.9244 - loss: 0.2723
9/60 āāāāāāāāāāāāāāāāāāāā 10s 205ms/step - accuracy: 0.9231 - loss: 0.2732
10/60 āāāāāāāāāāāāāāāāāāāā 10s 205ms/step - accuracy: 0.9211 - loss: 0.2752
11/60 āāāāāāāāāāāāāāāāāāāā 10s 205ms/step - accuracy: 0.9193 - loss: 0.2767
12/60 āāāāāāāāāāāāāāāāāāāā 9s 206ms/step - accuracy: 0.9175 - loss: 0.2786
13/60 āāāāāāāāāāāāāāāāāāāā 9s 206ms/step - accuracy: 0.9157 - loss: 0.2806
14/60 āāāāāāāāāāāāāāāāāāāā 9s 206ms/step - accuracy: 0.9138 - loss: 0.2829
15/60 āāāāāāāāāāāāāāāāāāāā 9s 206ms/step - accuracy: 0.9118 - loss: 0.2853
16/60 āāāāāāāāāāāāāāāāāāāā 9s 206ms/step - accuracy: 0.9101 - loss: 0.2876
17/60 āāāāāāāāāāāāāāāāāāāā 8s 205ms/step - accuracy: 0.9088 - loss: 0.2894
18/60 āāāāāāāāāāāāāāāāāāāā 8s 205ms/step - accuracy: 0.9075 - loss: 0.2908
19/60 āāāāāāāāāāāāāāāāāāāā 8s 205ms/step - accuracy: 0.9063 - loss: 0.2922
20/60 āāāāāāāāāāāāāāāāāāāā 8s 204ms/step - accuracy: 0.9051 - loss: 0.2935
21/60 āāāāāāāāāāāāāāāāāāāā 7s 204ms/step - accuracy: 0.9040 - loss: 0.2948
22/60 āāāāāāāāāāāāāāāāāāāā 7s 205ms/step - accuracy: 0.9031 - loss: 0.2958
23/60 āāāāāāāāāāāāāāāāāāāā 7s 204ms/step - accuracy: 0.9024 - loss: 0.2966
24/60 āāāāāāāāāāāāāāāāāāāā 7s 204ms/step - accuracy: 0.9017 - loss: 0.2973
25/60 āāāāāāāāāāāāāāāāāāāā 7s 204ms/step - accuracy: 0.9012 - loss: 0.2977
26/60 āāāāāāāāāāāāāāāāāāāā 6s 204ms/step - accuracy: 0.9008 - loss: 0.2980
27/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.9005 - loss: 0.2981
28/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.9002 - loss: 0.2983
29/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.8999 - loss: 0.2984
30/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.8996 - loss: 0.2986
31/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.8995 - loss: 0.2986
32/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.8993 - loss: 0.2986
33/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.8992 - loss: 0.2985
34/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.8992 - loss: 0.2984
35/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.8991 - loss: 0.2983
36/60 āāāāāāāāāāāāāāāāāāāā 4s 204ms/step - accuracy: 0.8991 - loss: 0.2980
37/60 āāāāāāāāāāāāāāāāāāāā 4s 204ms/step - accuracy: 0.8992 - loss: 0.2978
38/60 āāāāāāāāāāāāāāāāāāāā 4s 205ms/step - accuracy: 0.8991 - loss: 0.2976
39/60 āāāāāāāāāāāāāāāāāāāā 4s 204ms/step - accuracy: 0.8991 - loss: 0.2974
40/60 āāāāāāāāāāāāāāāāāāāā 4s 204ms/step - accuracy: 0.8990 - loss: 0.2972
41/60 āāāāāāāāāāāāāāāāāāāā 3s 204ms/step - accuracy: 0.8990 - loss: 0.2970
42/60 āāāāāāāāāāāāāāāāāāāā 3s 204ms/step - accuracy: 0.8990 - loss: 0.2967
43/60 āāāāāāāāāāāāāāāāāāāā 3s 204ms/step - accuracy: 0.8989 - loss: 0.2964
44/60 āāāāāāāāāāāāāāāāāāāā 3s 204ms/step - accuracy: 0.8989 - loss: 0.2961
45/60 āāāāāāāāāāāāāāāāāāāā 3s 204ms/step - accuracy: 0.8990 - loss: 0.2957
46/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.8990 - loss: 0.2953
47/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.8991 - loss: 0.2948
48/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.8992 - loss: 0.2944
49/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.8993 - loss: 0.2939
50/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.8994 - loss: 0.2933
51/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.8995 - loss: 0.2928
52/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.8996 - loss: 0.2923
53/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.8997 - loss: 0.2918
54/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.8998 - loss: 0.2913
55/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.8999 - loss: 0.2908
56/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.9000 - loss: 0.2903
57/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.9001 - loss: 0.2897
58/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.9002 - loss: 0.2892
59/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.9004 - loss: 0.2887
60/60 āāāāāāāāāāāāāāāāāāāā 0s 203ms/step - accuracy: 0.9005 - loss: 0.2882
60/60 āāāāāāāāāāāāāāāāāāāā 13s 219ms/step - accuracy: 0.9005 - loss: 0.2877 - val_accuracy: 0.8943 - val_loss: 0.2609
Epoch 4/5
1/60 āāāāāāāāāāāāāāāāāāāā 13s 222ms/step - accuracy: 0.9375 - loss: 0.2013
2/60 āāāāāāāāāāāāāāāāāāāā 11s 198ms/step - accuracy: 0.9375 - loss: 0.1923
3/60 āāāāāāāāāāāāāāāāāāāā 11s 200ms/step - accuracy: 0.9410 - loss: 0.1872
4/60 āāāāāāāāāāāāāāāāāāāā 11s 202ms/step - accuracy: 0.9440 - loss: 0.1827
5/60 āāāāāāāāāāāāāāāāāāāā 11s 201ms/step - accuracy: 0.9465 - loss: 0.1779
6/60 āāāāāāāāāāāāāāāāāāāā 10s 200ms/step - accuracy: 0.9467 - loss: 0.1761
7/60 āāāāāāāāāāāāāāāāāāāā 10s 201ms/step - accuracy: 0.9473 - loss: 0.1745
8/60 āāāāāāāāāāāāāāāāāāāā 10s 203ms/step - accuracy: 0.9485 - loss: 0.1720
9/60 āāāāāāāāāāāāāāāāāāāā 10s 204ms/step - accuracy: 0.9496 - loss: 0.1707
10/60 āāāāāāāāāāāāāāāāāāāā 10s 205ms/step - accuracy: 0.9506 - loss: 0.1698
11/60 āāāāāāāāāāāāāāāāāāāā 10s 205ms/step - accuracy: 0.9517 - loss: 0.1687
12/60 āāāāāāāāāāāāāāāāāāāā 9s 204ms/step - accuracy: 0.9525 - loss: 0.1679
13/60 āāāāāāāāāāāāāāāāāāāā 9s 204ms/step - accuracy: 0.9526 - loss: 0.1677
14/60 āāāāāāāāāāāāāāāāāāāā 9s 204ms/step - accuracy: 0.9530 - loss: 0.1671
15/60 āāāāāāāāāāāāāāāāāāāā 9s 204ms/step - accuracy: 0.9535 - loss: 0.1662
16/60 āāāāāāāāāāāāāāāāāāāā 8s 204ms/step - accuracy: 0.9541 - loss: 0.1652
17/60 āāāāāāāāāāāāāāāāāāāā 8s 204ms/step - accuracy: 0.9547 - loss: 0.1640
18/60 āāāāāāāāāāāāāāāāāāāā 8s 204ms/step - accuracy: 0.9551 - loss: 0.1633
19/60 āāāāāāāāāāāāāāāāāāāā 8s 203ms/step - accuracy: 0.9553 - loss: 0.1626
20/60 āāāāāāāāāāāāāāāāāāāā 8s 203ms/step - accuracy: 0.9555 - loss: 0.1619
21/60 āāāāāāāāāāāāāāāāāāāā 7s 204ms/step - accuracy: 0.9558 - loss: 0.1611
22/60 āāāāāāāāāāāāāāāāāāāā 7s 204ms/step - accuracy: 0.9560 - loss: 0.1605
23/60 āāāāāāāāāāāāāāāāāāāā 7s 204ms/step - accuracy: 0.9561 - loss: 0.1600
24/60 āāāāāāāāāāāāāāāāāāāā 7s 204ms/step - accuracy: 0.9563 - loss: 0.1596
25/60 āāāāāāāāāāāāāāāāāāāā 7s 205ms/step - accuracy: 0.9564 - loss: 0.1591
26/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.9566 - loss: 0.1585
27/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.9568 - loss: 0.1580
28/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.9570 - loss: 0.1574
29/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.9573 - loss: 0.1568
30/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.9574 - loss: 0.1563
31/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.9576 - loss: 0.1557
32/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.9578 - loss: 0.1551
33/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.9579 - loss: 0.1547
34/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.9580 - loss: 0.1542
35/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.9581 - loss: 0.1538
36/60 āāāāāāāāāāāāāāāāāāāā 4s 205ms/step - accuracy: 0.9583 - loss: 0.1533
37/60 āāāāāāāāāāāāāāāāāāāā 4s 205ms/step - accuracy: 0.9585 - loss: 0.1528
38/60 āāāāāāāāāāāāāāāāāāāā 4s 205ms/step - accuracy: 0.9586 - loss: 0.1524
39/60 āāāāāāāāāāāāāāāāāāāā 4s 205ms/step - accuracy: 0.9587 - loss: 0.1520
40/60 āāāāāāāāāāāāāāāāāāāā 4s 205ms/step - accuracy: 0.9588 - loss: 0.1516
41/60 āāāāāāāāāāāāāāāāāāāā 3s 205ms/step - accuracy: 0.9588 - loss: 0.1512
42/60 āāāāāāāāāāāāāāāāāāāā 3s 205ms/step - accuracy: 0.9589 - loss: 0.1508
43/60 āāāāāāāāāāāāāāāāāāāā 3s 205ms/step - accuracy: 0.9590 - loss: 0.1504
44/60 āāāāāāāāāāāāāāāāāāāā 3s 205ms/step - accuracy: 0.9591 - loss: 0.1500
45/60 āāāāāāāāāāāāāāāāāāāā 3s 205ms/step - accuracy: 0.9591 - loss: 0.1497
46/60 āāāāāāāāāāāāāāāāāāāā 2s 205ms/step - accuracy: 0.9592 - loss: 0.1493
47/60 āāāāāāāāāāāāāāāāāāāā 2s 205ms/step - accuracy: 0.9593 - loss: 0.1489
48/60 āāāāāāāāāāāāāāāāāāāā 2s 205ms/step - accuracy: 0.9593 - loss: 0.1486
49/60 āāāāāāāāāāāāāāāāāāāā 2s 205ms/step - accuracy: 0.9594 - loss: 0.1482
50/60 āāāāāāāāāāāāāāāāāāāā 2s 205ms/step - accuracy: 0.9595 - loss: 0.1479
51/60 āāāāāāāāāāāāāāāāāāāā 1s 205ms/step - accuracy: 0.9595 - loss: 0.1475
52/60 āāāāāāāāāāāāāāāāāāāā 1s 205ms/step - accuracy: 0.9595 - loss: 0.1472
53/60 āāāāāāāāāāāāāāāāāāāā 1s 205ms/step - accuracy: 0.9595 - loss: 0.1469
54/60 āāāāāāāāāāāāāāāāāāāā 1s 205ms/step - accuracy: 0.9595 - loss: 0.1466
55/60 āāāāāāāāāāāāāāāāāāāā 1s 205ms/step - accuracy: 0.9596 - loss: 0.1464
56/60 āāāāāāāāāāāāāāāāāāāā 0s 205ms/step - accuracy: 0.9596 - loss: 0.1461
57/60 āāāāāāāāāāāāāāāāāāāā 0s 205ms/step - accuracy: 0.9596 - loss: 0.1458
58/60 āāāāāāāāāāāāāāāāāāāā 0s 205ms/step - accuracy: 0.9597 - loss: 0.1455
59/60 āāāāāāāāāāāāāāāāāāāā 0s 205ms/step - accuracy: 0.9597 - loss: 0.1452
60/60 āāāāāāāāāāāāāāāāāāāā 0s 205ms/step - accuracy: 0.9597 - loss: 0.1449
60/60 āāāāāāāāāāāāāāāāāāāā 13s 221ms/step - accuracy: 0.9598 - loss: 0.1446 - val_accuracy: 0.9069 - val_loss: 0.2506
Epoch 5/5
1/60 āāāāāāāāāāāāāāāāāāāā 12s 206ms/step - accuracy: 1.0000 - loss: 0.0631
2/60 āāāāāāāāāāāāāāāāāāāā 12s 209ms/step - accuracy: 1.0000 - loss: 0.0601
3/60 āāāāāāāāāāāāāāāāāāāā 11s 205ms/step - accuracy: 0.9965 - loss: 0.0628
4/60 āāāāāāāāāāāāāāāāāāāā 11s 208ms/step - accuracy: 0.9935 - loss: 0.0647
5/60 āāāāāāāāāāāāāāāāāāāā 11s 205ms/step - accuracy: 0.9910 - loss: 0.0666
6/60 āāāāāāāāāāāāāāāāāāāā 11s 206ms/step - accuracy: 0.9891 - loss: 0.0685
7/60 āāāāāāāāāāāāāāāāāāāā 10s 206ms/step - accuracy: 0.9881 - loss: 0.0690
8/60 āāāāāāāāāāāāāāāāāāāā 10s 207ms/step - accuracy: 0.9876 - loss: 0.0694
9/60 āāāāāāāāāāāāāāāāāāāā 10s 205ms/step - accuracy: 0.9871 - loss: 0.0706
10/60 āāāāāāāāāāāāāāāāāāāā 10s 204ms/step - accuracy: 0.9865 - loss: 0.0715
11/60 āāāāāāāāāāāāāāāāāāāā 10s 205ms/step - accuracy: 0.9859 - loss: 0.0723
12/60 āāāāāāāāāāāāāāāāāāāā 9s 205ms/step - accuracy: 0.9849 - loss: 0.0733
13/60 āāāāāāāāāāāāāāāāāāāā 9s 205ms/step - accuracy: 0.9842 - loss: 0.0739
14/60 āāāāāāāāāāāāāāāāāāāā 9s 205ms/step - accuracy: 0.9837 - loss: 0.0743
15/60 āāāāāāāāāāāāāāāāāāāā 9s 204ms/step - accuracy: 0.9834 - loss: 0.0745
16/60 āāāāāāāāāāāāāāāāāāāā 9s 205ms/step - accuracy: 0.9831 - loss: 0.0745
17/60 āāāāāāāāāāāāāāāāāāāā 8s 204ms/step - accuracy: 0.9828 - loss: 0.0746
18/60 āāāāāāāāāāāāāāāāāāāā 8s 205ms/step - accuracy: 0.9825 - loss: 0.0747
19/60 āāāāāāāāāāāāāāāāāāāā 8s 205ms/step - accuracy: 0.9823 - loss: 0.0748
20/60 āāāāāāāāāāāāāāāāāāāā 8s 205ms/step - accuracy: 0.9822 - loss: 0.0748
21/60 āāāāāāāāāāāāāāāāāāāā 7s 205ms/step - accuracy: 0.9821 - loss: 0.0747
22/60 āāāāāāāāāāāāāāāāāāāā 7s 205ms/step - accuracy: 0.9821 - loss: 0.0746
23/60 āāāāāāāāāāāāāāāāāāāā 7s 205ms/step - accuracy: 0.9820 - loss: 0.0745
24/60 āāāāāāāāāāāāāāāāāāāā 7s 205ms/step - accuracy: 0.9820 - loss: 0.0743
25/60 āāāāāāāāāāāāāāāāāāāā 7s 205ms/step - accuracy: 0.9821 - loss: 0.0742
26/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.9820 - loss: 0.0741
27/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.9820 - loss: 0.0740
28/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.9820 - loss: 0.0740
29/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.9821 - loss: 0.0739
30/60 āāāāāāāāāāāāāāāāāāāā 6s 205ms/step - accuracy: 0.9821 - loss: 0.0739
31/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.9821 - loss: 0.0739
32/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.9821 - loss: 0.0738
33/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.9821 - loss: 0.0737
34/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.9821 - loss: 0.0737
35/60 āāāāāāāāāāāāāāāāāāāā 5s 205ms/step - accuracy: 0.9821 - loss: 0.0736
36/60 āāāāāāāāāāāāāāāāāāāā 4s 205ms/step - accuracy: 0.9821 - loss: 0.0736
37/60 āāāāāāāāāāāāāāāāāāāā 4s 205ms/step - accuracy: 0.9821 - loss: 0.0735
38/60 āāāāāāāāāāāāāāāāāāāā 4s 205ms/step - accuracy: 0.9821 - loss: 0.0734
39/60 āāāāāāāāāāāāāāāāāāāā 4s 205ms/step - accuracy: 0.9821 - loss: 0.0734
40/60 āāāāāāāāāāāāāāāāāāāā 4s 205ms/step - accuracy: 0.9821 - loss: 0.0733
41/60 āāāāāāāāāāāāāāāāāāāā 3s 205ms/step - accuracy: 0.9822 - loss: 0.0732
42/60 āāāāāāāāāāāāāāāāāāāā 3s 205ms/step - accuracy: 0.9822 - loss: 0.0731
43/60 āāāāāāāāāāāāāāāāāāāā 3s 204ms/step - accuracy: 0.9822 - loss: 0.0730
44/60 āāāāāāāāāāāāāāāāāāāā 3s 204ms/step - accuracy: 0.9822 - loss: 0.0730
45/60 āāāāāāāāāāāāāāāāāāāā 3s 204ms/step - accuracy: 0.9823 - loss: 0.0729
46/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.9823 - loss: 0.0728
47/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.9823 - loss: 0.0727
48/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.9823 - loss: 0.0727
49/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.9823 - loss: 0.0726
50/60 āāāāāāāāāāāāāāāāāāāā 2s 204ms/step - accuracy: 0.9823 - loss: 0.0725
51/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.9823 - loss: 0.0725
52/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.9823 - loss: 0.0725
53/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.9822 - loss: 0.0725
54/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.9822 - loss: 0.0725
55/60 āāāāāāāāāāāāāāāāāāāā 1s 204ms/step - accuracy: 0.9822 - loss: 0.0725
56/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.9822 - loss: 0.0725
57/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.9822 - loss: 0.0725
58/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.9821 - loss: 0.0725
59/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.9821 - loss: 0.0725
60/60 āāāāāāāāāāāāāāāāāāāā 0s 204ms/step - accuracy: 0.9821 - loss: 0.0725
60/60 āāāāāāāāāāāāāāāāāāāā 13s 220ms/step - accuracy: 0.9821 - loss: 0.0725 - val_accuracy: 0.9054 - val_loss: 0.2631
1/20 āāāāāāāāāāāāāāāāāāāā 2s 121ms/step
3/20 āāāāāāāāāāāāāāāāāāāā 0s 52ms/step
5/20 āāāāāāāāāāāāāāāāāāāā 0s 49ms/step
6/20 āāāāāāāāāāāāāāāāāāāā 0s 50ms/step
8/20 āāāāāāāāāāāāāāāāāāāā 0s 48ms/step
10/20 āāāāāāāāāāāāāāāāāāāā 0s 47ms/step
11/20 āāāāāāāāāāāāāāāāāāāā 0s 47ms/step
13/20 āāāāāāāāāāāāāāāāāāāā 0s 47ms/step
15/20 āāāāāāāāāāāāāāāāāāāā 0s 47ms/step
17/20 āāāāāāāāāāāāāāāāāāāā 0s 46ms/step
19/20 āāāāāāāāāāāāāāāāāāāā 0s 46ms/step
20/20 āāāāāāāāāāāāāāāāāāāā 0s 50ms/step
20/20 āāāāāāāāāāāāāāāāāāāā 1s 50ms/step
Accuracy: 0.9053627760252366 Precision: 0.8802395209580839 Recall: 0.9363057324840764
Epoch 1/5
1/67 āāāāāāāāāāāāāāāāāāāā 1:16 1s/step - accuracy: 0.5000 - loss: 0.6940
2/67 āāāāāāāāāāāāāāāāāāāā 11s 174ms/step - accuracy: 0.5703 - loss: 0.6932
3/67 āāāāāāāāāāāāāāāāāāāā 11s 178ms/step - accuracy: 0.5885 - loss: 0.6929
4/67 āāāāāāāāāāāāāāāāāāāā 11s 182ms/step - accuracy: 0.5859 - loss: 0.6928
5/67 āāāāāāāāāāāāāāāāāāāā 11s 180ms/step - accuracy: 0.5850 - loss: 0.6927
6/67 āāāāāāāāāāāāāāāāāāāā 10s 179ms/step - accuracy: 0.5847 - loss: 0.6927
7/67 āāāāāāāāāāāāāāāāāāāā 10s 179ms/step - accuracy: 0.5886 - loss: 0.6924
8/67 āāāāāāāāāāāāāāāāāāāā 10s 178ms/step - accuracy: 0.5912 - loss: 0.6923
9/67 āāāāāāāāāāāāāāāāāāāā 10s 178ms/step - accuracy: 0.5930 - loss: 0.6921
10/67 āāāāāāāāāāāāāāāāāāāā 10s 177ms/step - accuracy: 0.5937 - loss: 0.6920
11/67 āāāāāāāāāāāāāāāāāāāā 9s 177ms/step - accuracy: 0.5950 - loss: 0.6918
12/67 āāāāāāāāāāāāāāāāāāāā 9s 177ms/step - accuracy: 0.5947 - loss: 0.6917
13/67 āāāāāāāāāāāāāāāāāāāā 9s 177ms/step - accuracy: 0.5944 - loss: 0.6915
14/67 āāāāāāāāāāāāāāāāāāāā 9s 178ms/step - accuracy: 0.5937 - loss: 0.6914
15/67 āāāāāāāāāāāāāāāāāāāā 9s 178ms/step - accuracy: 0.5925 - loss: 0.6914
16/67 āāāāāāāāāāāāāāāāāāāā 9s 178ms/step - accuracy: 0.5912 - loss: 0.6913
17/67 āāāāāāāāāāāāāāāāāāāā 8s 178ms/step - accuracy: 0.5903 - loss: 0.6913
18/67 āāāāāāāāāāāāāāāāāāāā 8s 178ms/step - accuracy: 0.5893 - loss: 0.6912
19/67 āāāāāāāāāāāāāāāāāāāā 8s 178ms/step - accuracy: 0.5888 - loss: 0.6911
20/67 āāāāāāāāāāāāāāāāāāāā 8s 178ms/step - accuracy: 0.5882 - loss: 0.6910
21/67 āāāāāāāāāāāāāāāāāāāā 8s 178ms/step - accuracy: 0.5872 - loss: 0.6910
22/67 āāāāāāāāāāāāāāāāāāāā 7s 177ms/step - accuracy: 0.5862 - loss: 0.6909
23/67 āāāāāāāāāāāāāāāāāāāā 7s 177ms/step - accuracy: 0.5857 - loss: 0.6908
24/67 āāāāāāāāāāāāāāāāāāāā 7s 177ms/step - accuracy: 0.5853 - loss: 0.6907
25/67 āāāāāāāāāāāāāāāāāāāā 7s 177ms/step - accuracy: 0.5850 - loss: 0.6906
26/67 āāāāāāāāāāāāāāāāāāāā 7s 177ms/step - accuracy: 0.5847 - loss: 0.6904
27/67 āāāāāāāāāāāāāāāāāāāā 7s 177ms/step - accuracy: 0.5846 - loss: 0.6902
28/67 āāāāāāāāāāāāāāāāāāāā 6s 177ms/step - accuracy: 0.5847 - loss: 0.6900
29/67 āāāāāāāāāāāāāāāāāāāā 6s 177ms/step - accuracy: 0.5849 - loss: 0.6898
30/67 āāāāāāāāāāāāāāāāāāāā 6s 177ms/step - accuracy: 0.5851 - loss: 0.6896
31/67 āāāāāāāāāāāāāāāāāāāā 6s 177ms/step - accuracy: 0.5852 - loss: 0.6894
32/67 āāāāāāāāāāāāāāāāāāāā 6s 177ms/step - accuracy: 0.5855 - loss: 0.6891
33/67 āāāāāāāāāāāāāāāāāāāā 6s 177ms/step - accuracy: 0.5858 - loss: 0.6889
34/67 āāāāāāāāāāāāāāāāāāāā 5s 177ms/step - accuracy: 0.5862 - loss: 0.6885
35/67 āāāāāāāāāāāāāāāāāāāā 5s 177ms/step - accuracy: 0.5866 - loss: 0.6882
36/67 āāāāāāāāāāāāāāāāāāāā 5s 177ms/step - accuracy: 0.5869 - loss: 0.6878
37/67 āāāāāāāāāāāāāāāāāāāā 5s 177ms/step - accuracy: 0.5873 - loss: 0.6875
38/67 āāāāāāāāāāāāāāāāāāāā 5s 177ms/step - accuracy: 0.5877 - loss: 0.6872
39/67 āāāāāāāāāāāāāāāāāāāā 4s 177ms/step - accuracy: 0.5881 - loss: 0.6868
40/67 āāāāāāāāāāāāāāāāāāāā 4s 177ms/step - accuracy: 0.5886 - loss: 0.6864
41/67 āāāāāāāāāāāāāāāāāāāā 4s 177ms/step - accuracy: 0.5892 - loss: 0.6859
42/67 āāāāāāāāāāāāāāāāāāāā 4s 177ms/step - accuracy: 0.5898 - loss: 0.6854
43/67 āāāāāāāāāāāāāāāāāāāā 4s 177ms/step - accuracy: 0.5904 - loss: 0.6849
44/67 āāāāāāāāāāāāāāāāāāāā 4s 177ms/step - accuracy: 0.5911 - loss: 0.6844
45/67 āāāāāāāāāāāāāāāāāāāā 3s 177ms/step - accuracy: 0.5918 - loss: 0.6838
46/67 āāāāāāāāāāāāāāāāāāāā 3s 177ms/step - accuracy: 0.5925 - loss: 0.6832
47/67 āāāāāāāāāāāāāāāāāāāā 3s 177ms/step - accuracy: 0.5932 - loss: 0.6826
48/67 āāāāāāāāāāāāāāāāāāāā 3s 177ms/step - accuracy: 0.5940 - loss: 0.6820
49/67 āāāāāāāāāāāāāāāāāāāā 3s 177ms/step - accuracy: 0.5948 - loss: 0.6813
50/67 āāāāāāāāāāāāāāāāāāāā 2s 176ms/step - accuracy: 0.5956 - loss: 0.6806
51/67 āāāāāāāāāāāāāāāāāāāā 2s 176ms/step - accuracy: 0.5964 - loss: 0.6798
52/67 āāāāāāāāāāāāāāāāāāāā 2s 176ms/step - accuracy: 0.5972 - loss: 0.6791
53/67 āāāāāāāāāāāāāāāāāāāā 2s 176ms/step - accuracy: 0.5980 - loss: 0.6784
54/67 āāāāāāāāāāāāāāāāāāāā 2s 176ms/step - accuracy: 0.5988 - loss: 0.6777
55/67 āāāāāāāāāāāāāāāāāāāā 2s 176ms/step - accuracy: 0.5996 - loss: 0.6769
56/67 āāāāāāāāāāāāāāāāāāāā 1s 176ms/step - accuracy: 0.6003 - loss: 0.6763
57/67 āāāāāāāāāāāāāāāāāāāā 1s 176ms/step - accuracy: 0.6011 - loss: 0.6756
58/67 āāāāāāāāāāāāāāāāāāāā 1s 176ms/step - accuracy: 0.6018 - loss: 0.6749
59/67 āāāāāāāāāāāāāāāāāāāā 1s 176ms/step - accuracy: 0.6026 - loss: 0.6742
60/67 āāāāāāāāāāāāāāāāāāāā 1s 176ms/step - accuracy: 0.6033 - loss: 0.6735
61/67 āāāāāāāāāāāāāāāāāāāā 1s 176ms/step - accuracy: 0.6041 - loss: 0.6728
62/67 āāāāāāāāāāāāāāāāāāāā 0s 176ms/step - accuracy: 0.6048 - loss: 0.6722
63/67 āāāāāāāāāāāāāāāāāāāā 0s 176ms/step - accuracy: 0.6055 - loss: 0.6715
64/67 āāāāāāāāāāāāāāāāāāāā 0s 176ms/step - accuracy: 0.6062 - loss: 0.6708
65/67 āāāāāāāāāāāāāāāāāāāā 0s 176ms/step - accuracy: 0.6069 - loss: 0.6701
66/67 āāāāāāāāāāāāāāāāāāāā 0s 176ms/step - accuracy: 0.6076 - loss: 0.6694
67/67 āāāāāāāāāāāāāāāāāāāā 0s 176ms/step - accuracy: 0.6083 - loss: 0.6687
67/67 āāāāāāāāāāāāāāāāāāāā 14s 192ms/step - accuracy: 0.6089 - loss: 0.6680 - val_accuracy: 0.7603 - val_loss: 0.5031
Epoch 2/5
1/67 āāāāāāāāāāāāāāāāāāāā 11s 181ms/step - accuracy: 0.8125 - loss: 0.4371
2/67 āāāāāāāāāāāāāāāāāāāā 11s 174ms/step - accuracy: 0.8047 - loss: 0.4446
3/67 āāāāāāāāāāāāāāāāāāāā 11s 177ms/step - accuracy: 0.7830 - loss: 0.4610
4/67 āāāāāāāāāāāāāāāāāāāā 11s 175ms/step - accuracy: 0.7708 - loss: 0.4692
5/67 āāāāāāāāāāāāāāāāāāāā 10s 176ms/step - accuracy: 0.7642 - loss: 0.4737
6/67 āāāāāāāāāāāāāāāāāāāā 10s 176ms/step - accuracy: 0.7505 - loss: 0.5042
7/67 āāāāāāāāāāāāāāāāāāāā 10s 177ms/step - accuracy: 0.7358 - loss: 0.5403
8/67 āāāāāāāāāāāāāāāāāāāā 10s 177ms/step - accuracy: 0.7224 - loss: 0.5741
9/67 āāāāāāāāāāāāāāāāāāāā 10s 176ms/step - accuracy: 0.7101 - loss: 0.6050
10/67 āāāāāāāāāāāāāāāāāāāā 10s 176ms/step - accuracy: 0.6978 - loss: 0.6335
11/67 āāāāāāāāāāāāāāāāāāāā 9s 175ms/step - accuracy: 0.6863 - loss: 0.6589
12/67 āāāāāāāāāāāāāāāāāāāā 9s 175ms/step - accuracy: 0.6760 - loss: 0.6800
13/67 āāāāāāāāāāāāāāāāāāāā 9s 175ms/step - accuracy: 0.6672 - loss: 0.6963
14/67 āāāāāāāāāāāāāāāāāāāā 9s 175ms/step - accuracy: 0.6610 - loss: 0.7080
15/67 āāāāāāāāāāāāāāāāāāāā 9s 176ms/step - accuracy: 0.6568 - loss: 0.7166
16/67 āāāāāāāāāāāāāāāāāāāā 8s 176ms/step - accuracy: 0.6537 - loss: 0.7229
17/67 āāāāāāāāāāāāāāāāāāāā 8s 175ms/step - accuracy: 0.6517 - loss: 0.7273
18/67 āāāāāāāāāāāāāāāāāāāā 8s 176ms/step - accuracy: 0.6499 - loss: 0.7307
19/67 āāāāāāāāāāāāāāāāāāāā 8s 175ms/step - accuracy: 0.6490 - loss: 0.7331
20/67 āāāāāāāāāāāāāāāāāāāā 8s 176ms/step - accuracy: 0.6484 - loss: 0.7348
21/67 āāāāāāāāāāāāāāāāāāāā 8s 176ms/step - accuracy: 0.6482 - loss: 0.7358
22/67 āāāāāāāāāāāāāāāāāāāā 7s 176ms/step - accuracy: 0.6486 - loss: 0.7362
23/67 āāāāāāāāāāāāāāāāāāāā 7s 176ms/step - accuracy: 0.6490 - loss: 0.7363
24/67 āāāāāāāāāāāāāāāāāāāā 7s 177ms/step - accuracy: 0.6497 - loss: 0.7360
25/67 āāāāāāāāāāāāāāāāāāāā 7s 177ms/step - accuracy: 0.6506 - loss: 0.7355
26/67 āāāāāāāāāāāāāāāāāāāā 7s 177ms/step - accuracy: 0.6516 - loss: 0.7348
27/67 āāāāāāāāāāāāāāāāāāāā 7s 177ms/step - accuracy: 0.6526 - loss: 0.7339
28/67 āāāāāāāāāāāāāāāāāāāā 6s 177ms/step - accuracy: 0.6537 - loss: 0.7329
29/67 āāāāāāāāāāāāāāāāāāāā 6s 177ms/step - accuracy: 0.6549 - loss: 0.7317
30/67 āāāāāāāāāāāāāāāāāāāā 6s 177ms/step - accuracy: 0.6562 - loss: 0.7305
31/67 āāāāāāāāāāāāāāāāāāāā 6s 177ms/step - accuracy: 0.6576 - loss: 0.7291
32/67 āāāāāāāāāāāāāāāāāāāā 6s 177ms/step - accuracy: 0.6589 - loss: 0.7278
33/67 āāāāāāāāāāāāāāāāāāāā 6s 177ms/step - accuracy: 0.6601 - loss: 0.7265
34/67 āāāāāāāāāāāāāāāāāāāā 5s 177ms/step - accuracy: 0.6613 - loss: 0.7252
35/67 āāāāāāāāāāāāāāāāāāāā 5s 176ms/step - accuracy: 0.6625 - loss: 0.7238
36/67 āāāāāāāāāāāāāāāāāāāā 5s 176ms/step - accuracy: 0.6637 - loss: 0.7225
37/67 āāāāāāāāāāāāāāāāāāāā 5s 176ms/step - accuracy: 0.6648 - loss: 0.7212
38/67 āāāāāāāāāāāāāāāāāāāā 5s 176ms/step - accuracy: 0.6659 - loss: 0.7200
39/67 āāāāāāāāāāāāāāāāāāāā 4s 176ms/step - accuracy: 0.6671 - loss: 0.7187
40/67 āāāāāāāāāāāāāāāāāāāā 4s 176ms/step - accuracy: 0.6682 - loss: 0.7173
41/67 āāāāāāāāāāāāāāāāāāāā 4s 176ms/step - accuracy: 0.6693 - loss: 0.7160
42/67 āāāāāāāāāāāāāāāāāāāā 4s 176ms/step - accuracy: 0.6703 - loss: 0.7148
43/67 āāāāāāāāāāāāāāāāāāāā 4s 176ms/step - accuracy: 0.6714 - loss: 0.7135
44/67 āāāāāāāāāāāāāāāāāāāā 4s 176ms/step - accuracy: 0.6724 - loss: 0.7122
45/67 āāāāāāāāāāāāāāāāāāāā 3s 176ms/step - accuracy: 0.6735 - loss: 0.7108
46/67 āāāāāāāāāāāāāāāāāāāā 3s 176ms/step - accuracy: 0.6745 - loss: 0.7096
47/67 āāāāāāāāāāāāāāāāāāāā 3s 176ms/step - accuracy: 0.6754 - loss: 0.7083
48/67 āāāāāāāāāāāāāāāāāāāā 3s 176ms/step - accuracy: 0.6763 - loss: 0.7070
49/67 āāāāāāāāāāāāāāāāāāāā 3s 176ms/step - accuracy: 0.6772 - loss: 0.7057
50/67 āāāāāāāāāāāāāāāāāāāā 2s 176ms/step - accuracy: 0.6782 - loss: 0.7044
51/67 āāāāāāāāāāāāāāāāāāāā 2s 176ms/step - accuracy: 0.6792 - loss: 0.7030
52/67 āāāāāāāāāāāāāāāāāāāā 2s 176ms/step - accuracy: 0.6801 - loss: 0.7017
53/67 āāāāāāāāāāāāāāāāāāāā 2s 176ms/step - accuracy: 0.6811 - loss: 0.7004
54/67 āāāāāāāāāāāāāāāāāāāā 2s 176ms/step - accuracy: 0.6820 - loss: 0.6991
55/67 āāāāāāāāāāāāāāāāāāāā 2s 176ms/step - accuracy: 0.6829 - loss: 0.6978
56/67 āāāāāāāāāāāāāāāāāāāā 1s 176ms/step - accuracy: 0.6838 - loss: 0.6964
57/67 āāāāāāāāāāāāāāāāāāāā 1s 177ms/step - accuracy: 0.6848 - loss: 0.6951
58/67 āāāāāāāāāāāāāāāāāāāā 1s 177ms/step - accuracy: 0.6857 - loss: 0.6938
59/67 āāāāāāāāāāāāāāāāāāāā 1s 176ms/step - accuracy: 0.6866 - loss: 0.6925
60/67 āāāāāāāāāāāāāāāāāāāā 1s 177ms/step - accuracy: 0.6875 - loss: 0.6912
61/67 āāāāāāāāāāāāāāāāāāāā 1s 177ms/step - accuracy: 0.6884 - loss: 0.6899
62/67 āāāāāāāāāāāāāāāāāāāā 0s 176ms/step - accuracy: 0.6893 - loss: 0.6886
63/67 āāāāāāāāāāāāāāāāāāāā 0s 176ms/step - accuracy: 0.6902 - loss: 0.6873
64/67 āāāāāāāāāāāāāāāāāāāā 0s 176ms/step - accuracy: 0.6911 - loss: 0.6861
65/67 āāāāāāāāāāāāāāāāāāāā 0s 176ms/step - accuracy: 0.6920 - loss: 0.6848
66/67 āāāāāāāāāāāāāāāāāāāā 0s 176ms/step - accuracy: 0.6929 - loss: 0.6835
67/67 āāāāāāāāāāāāāāāāāāāā 0s 176ms/step - accuracy: 0.6937 - loss: 0.6822
67/67 āāāāāāāāāāāāāāāāāāāā 13s 190ms/step - accuracy: 0.6945 - loss: 0.6809 - val_accuracy: 0.8057 - val_loss: 0.4449
Epoch 3/5
1/67 āāāāāāāāāāāāāāāāāāāā 11s 179ms/step - accuracy: 0.8750 - loss: 0.4340
2/67 āāāāāāāāāāāāāāāāāāāā 11s 177ms/step - accuracy: 0.8594 - loss: 0.4469
3/67 āāāāāāāāāāāāāāāāāāāā 11s 174ms/step - accuracy: 0.8472 - loss: 0.4585
4/67 āāāāāāāāāāāāāāāāāāāā 11s 175ms/step - accuracy: 0.8405 - loss: 0.4611
5/67 āāāāāāāāāāāāāāāāāāāā 10s 174ms/step - accuracy: 0.8374 - loss: 0.4609
6/67 āāāāāāāāāāāāāāāāāāāā 10s 173ms/step - accuracy: 0.8350 - loss: 0.4607
7/67 āāāāāāāāāāāāāāāāāāāā 10s 174ms/step - accuracy: 0.8330 - loss: 0.4607
8/67 āāāāāāāāāāāāāāāāāāāā 10s 175ms/step - accuracy: 0.8329 - loss: 0.4591
9/67 āāāāāāāāāāāāāāāāāāāā 10s 175ms/step - accuracy: 0.8345 - loss: 0.4560
10/67 āāāāāāāāāāāāāāāāāāāā 9s 174ms/step - accuracy: 0.8351 - loss: 0.4540
11/67 āāāāāāāāāāāāāāāāāāāā 9s 175ms/step - accuracy: 0.8364 - loss: 0.4511
12/67 āāāāāāāāāāāāāāāāāāāā 9s 175ms/step - accuracy: 0.8381 - loss: 0.4479
13/67 āāāāāāāāāāāāāāāāāāāā 9s 176ms/step - accuracy: 0.8389 - loss: 0.4455
14/67 āāāāāāāāāāāāāāāāāāāā 9s 176ms/step - accuracy: 0.8388 - loss: 0.4444
15/67 āāāāāāāāāāāāāāāāāāāā 9s 176ms/step - accuracy: 0.8393 - loss: 0.4428
16/67 āāāāāāāāāāāāāāāāāāāā 8s 176ms/step - accuracy: 0.8394 - loss: 0.4415
17/67 āāāāāāāāāāāāāāāāāāāā 8s 176ms/step - accuracy: 0.8397 - loss: 0.4401
18/67 āāāāāāāāāāāāāāāāāāāā 8s 176ms/step - accuracy: 0.8399 - loss: 0.4388
19/67 āāāāāāāāāāāāāāāāāāāā 8s 176ms/step - accuracy: 0.8400 - loss: 0.4375
20/67 āāāāāāāāāāāāāāāāāāāā 8s 176ms/step - accuracy: 0.8401 - loss: 0.4363
21/67 āāāāāāāāāāāāāāāāāāāā 8s 176ms/step - accuracy: 0.8402 - loss: 0.4350
22/67 āāāāāāāāāāāāāāāāāāāā 7s 176ms/step - accuracy: 0.8401 - loss: 0.4341
23/67 āāāāāāāāāāāāāāāāāāāā 7s 176ms/step - accuracy: 0.8398 - loss: 0.4336
24/67 āāāāāāāāāāāāāāāāāāāā 7s 176ms/step - accuracy: 0.8395 - loss: 0.4331
25/67 āāāāāāāāāāāāāāāāāāāā 7s 176ms/step - accuracy: 0.8393 - loss: 0.4325
26/67 āāāāāāāāāāāāāāāāāāāā 7s 176ms/step - accuracy: 0.8391 - loss: 0.4318
27/67 āāāāāāāāāāāāāāāāāāāā 7s 176ms/step - accuracy: 0.8389 - loss: 0.4313
28/67 āāāāāāāāāāāāāāāāāāāā 6s 176ms/step - accuracy: 0.8389 - loss: 0.4306
29/67 āāāāāāāāāāāāāāāāāāāā 6s 176ms/step - accuracy: 0.8389 - loss: 0.4300
30/67 āāāāāāāāāāāāāāāāāāāā 6s 175ms/step - accuracy: 0.8388 - loss: 0.4294
31/67 āāāāāāāāāāāāāāāāāāāā 6s 175ms/step - accuracy: 0.8387 - loss: 0.4289
32/67 āāāāāāāāāāāāāāāāāāāā 6s 175ms/step - accuracy: 0.8386 - loss: 0.4283
33/67 āāāāāāāāāāāāāāāāāāāā 5s 176ms/step - accuracy: 0.8387 - loss: 0.4276
34/67 āāāāāāāāāāāāāāāāāāāā 5s 176ms/step - accuracy: 0.8388 - loss: 0.4268
35/67 āāāāāāāāāāāāāāāāāāāā 5s 175ms/step - accuracy: 0.8389 - loss: 0.4260
36/67 āāāāāāāāāāāāāāāāāāāā 5s 175ms/step - accuracy: 0.8389 - loss: 0.4253
37/67 āāāāāāāāāāāāāāāāāāāā 5s 175ms/step - accuracy: 0.8390 - loss: 0.4246
38/67 āāāāāāāāāāāāāāāāāāāā 5s 175ms/step - accuracy: 0.8390 - loss: 0.4239
39/67 āāāāāāāāāāāāāāāāāāāā 4s 175ms/step - accuracy: 0.8391 - loss: 0.4231
40/67 āāāāāāāāāāāāāāāāāāāā 4s 175ms/step - accuracy: 0.8392 - loss: 0.4224
41/67 āāāāāāāāāāāāāāāāāāāā 4s 175ms/step - accuracy: 0.8393 - loss: 0.4216
42/67 āāāāāāāāāāāāāāāāāāāā 4s 175ms/step - accuracy: 0.8394 - loss: 0.4210
43/67 āāāāāāāāāāāāāāāāāāāā 4s 175ms/step - accuracy: 0.8395 - loss: 0.4202
44/67 āāāāāāāāāāāāāāāāāāāā 4s 175ms/step - accuracy: 0.8396 - loss: 0.4195
45/67 āāāāāāāāāāāāāāāāāāāā 3s 175ms/step - accuracy: 0.8397 - loss: 0.4187
46/67 āāāāāāāāāāāāāāāāāāāā 3s 175ms/step - accuracy: 0.8398 - loss: 0.4181
47/67 āāāāāāāāāāāāāāāāāāāā 3s 175ms/step - accuracy: 0.8399 - loss: 0.4174
48/67 āāāāāāāāāāāāāāāāāāāā 3s 175ms/step - accuracy: 0.8400 - loss: 0.4167
49/67 āāāāāāāāāāāāāāāāāāāā 3s 175ms/step - accuracy: 0.8401 - loss: 0.4159
50/67 āāāāāāāāāāāāāāāāāāāā 2s 175ms/step - accuracy: 0.8403 - loss: 0.4152
51/67 āāāāāāāāāāāāāāāāāāāā 2s 175ms/step - accuracy: 0.8404 - loss: 0.4145
52/67 āāāāāāāāāāāāāāāāāāāā 2s 175ms/step - accuracy: 0.8405 - loss: 0.4138
53/67 āāāāāāāāāāāāāāāāāāāā 2s 175ms/step - accuracy: 0.8407 - loss: 0.4131
54/67 āāāāāāāāāāāāāāāāāāāā 2s 175ms/step - accuracy: 0.8408 - loss: 0.4124
55/67 āāāāāāāāāāāāāāāāāāāā 2s 175ms/step - accuracy: 0.8410 - loss: 0.4118
56/67 āāāāāāāāāāāāāāāāāāāā 1s 175ms/step - accuracy: 0.8411 - loss: 0.4112
57/67 āāāāāāāāāāāāāāāāāāāā 1s 175ms/step - accuracy: 0.8413 - loss: 0.4105
58/67 āāāāāāāāāāāāāāāāāāāā 1s 175ms/step - accuracy: 0.8415 - loss: 0.4098
59/67 āāāāāāāāāāāāāāāāāāāā 1s 175ms/step - accuracy: 0.8417 - loss: 0.4092
60/67 āāāāāāāāāāāāāāāāāāāā 1s 175ms/step - accuracy: 0.8418 - loss: 0.4086
61/67 āāāāāāāāāāāāāāāāāāāā 1s 175ms/step - accuracy: 0.8419 - loss: 0.4080
62/67 āāāāāāāāāāāāāāāāāāāā 0s 176ms/step - accuracy: 0.8421 - loss: 0.4074
63/67 āāāāāāāāāāāāāāāāāāāā 0s 175ms/step - accuracy: 0.8422 - loss: 0.4068
64/67 āāāāāāāāāāāāāāāāāāāā 0s 175ms/step - accuracy: 0.8424 - loss: 0.4062
65/67 āāāāāāāāāāāāāāāāāāāā 0s 175ms/step - accuracy: 0.8426 - loss: 0.4056
66/67 āāāāāāāāāāāāāāāāāāāā 0s 175ms/step - accuracy: 0.8427 - loss: 0.4051
67/67 āāāāāāāāāāāāāāāāāāāā 0s 175ms/step - accuracy: 0.8429 - loss: 0.4044
67/67 āāāāāāāāāāāāāāāāāāāā 13s 190ms/step - accuracy: 0.8431 - loss: 0.4038 - val_accuracy: 0.8652 - val_loss: 0.3413
Epoch 4/5
1/67 āāāāāāāāāāāāāāāāāāāā 12s 194ms/step - accuracy: 0.8750 - loss: 0.2793
2/67 āāāāāāāāāāāāāāāāāāāā 12s 188ms/step - accuracy: 0.8984 - loss: 0.2638
3/67 āāāāāāāāāāāāāāāāāāāā 11s 178ms/step - accuracy: 0.9045 - loss: 0.2578
4/67 āāāāāāāāāāāāāāāāāāāā 11s 176ms/step - accuracy: 0.9089 - loss: 0.2565
5/67 āāāāāāāāāāāāāāāāāāāā 10s 176ms/step - accuracy: 0.9121 - loss: 0.2535
6/67 āāāāāāāāāāāāāāāāāāāā 10s 177ms/step - accuracy: 0.9111 - loss: 0.2578
7/67 āāāāāāāāāāāāāāāāāāāā 10s 177ms/step - accuracy: 0.9117 - loss: 0.2599
8/67 āāāāāāāāāāāāāāāāāāāā 10s 177ms/step - accuracy: 0.9115 - loss: 0.2619
9/67 āāāāāāāāāāāāāāāāāāāā 10s 177ms/step - accuracy: 0.9121 - loss: 0.2625
10/67 āāāāāāāāāāāāāāāāāāāā 10s 178ms/step - accuracy: 0.9121 - loss: 0.2625
11/67 āāāāāāāāāāāāāāāāāāāā 9s 178ms/step - accuracy: 0.9116 - loss: 0.2636
12/67 āāāāāāāāāāāāāāāāāāāā 9s 177ms/step - accuracy: 0.9118 - loss: 0.2633
13/67 āāāāāāāāāāāāāāāāāāāā 9s 177ms/step - accuracy: 0.9119 - loss: 0.2629
14/67 āāāāāāāāāāāāāāāāāāāā 9s 177ms/step - accuracy: 0.9122 - loss: 0.2625
15/67 āāāāāāāāāāāāāāāāāāāā 9s 176ms/step - accuracy: 0.9123 - loss: 0.2619
16/67 āāāāāāāāāāāāāāāāāāāā 9s 176ms/step - accuracy: 0.9123 - loss: 0.2614
17/67 āāāāāāāāāāāāāāāāāāāā 8s 177ms/step - accuracy: 0.9124 - loss: 0.2610
18/67 āāāāāāāāāāāāāāāāāāāā 8s 177ms/step - accuracy: 0.9121 - loss: 0.2611
19/67 āāāāāāāāāāāāāāāāāāāā 8s 177ms/step - accuracy: 0.9119 - loss: 0.2609
20/67 āāāāāāāāāāāāāāāāāāāā 8s 177ms/step - accuracy: 0.9117 - loss: 0.2607
21/67 āāāāāāāāāāāāāāāāāāāā 8s 177ms/step - accuracy: 0.9117 - loss: 0.2604
22/67 āāāāāāāāāāāāāāāāāāāā 7s 177ms/step - accuracy: 0.9118 - loss: 0.2600
23/67 āāāāāāāāāāāāāāāāāāāā 7s 177ms/step - accuracy: 0.9119 - loss: 0.2596
24/67 āāāāāāāāāāāāāāāāāāāā 7s 177ms/step - accuracy: 0.9121 - loss: 0.2593
25/67 āāāāāāāāāāāāāāāāāāāā 7s 177ms/step - accuracy: 0.9122 - loss: 0.2590
26/67 āāāāāāāāāāāāāāāāāāāā 7s 177ms/step - accuracy: 0.9123 - loss: 0.2587
27/67 āāāāāāāāāāāāāāāāāāāā 7s 176ms/step - accuracy: 0.9124 - loss: 0.2584
28/67 āāāāāāāāāāāāāāāāāāāā 6s 176ms/step - accuracy: 0.9126 - loss: 0.2579
29/67 āāāāāāāāāāāāāāāāāāāā 6s 176ms/step - accuracy: 0.9128 - loss: 0.2575
30/67 āāāāāāāāāāāāāāāāāāāā 6s 176ms/step - accuracy: 0.9130 - loss: 0.2571
31/67 āāāāāāāāāāāāāāāāāāāā 6s 176ms/step - accuracy: 0.9132 - loss: 0.2566
32/67 āāāāāāāāāāāāāāāāāāāā 6s 176ms/step - accuracy: 0.9134 - loss: 0.2560
33/67 āāāāāāāāāāāāāāāāāāāā 5s 176ms/step - accuracy: 0.9136 - loss: 0.2555
34/67 āāāāāāāāāāāāāāāāāāāā 5s 176ms/step - accuracy: 0.9139 - loss: 0.2549
35/67 āāāāāāāāāāāāāāāāāāāā 5s 176ms/step - accuracy: 0.9142 - loss: 0.2544
36/67 āāāāāāāāāāāāāāāāāāāā 5s 176ms/step - accuracy: 0.9144 - loss: 0.2538
37/67 āāāāāāāāāāāāāāāāāāāā 5s 175ms/step - accuracy: 0.9147 - loss: 0.2532
38/67 āāāāāāāāāāāāāāāāāāāā 5s 175ms/step - accuracy: 0.9150 - loss: 0.2527
39/67 āāāāāāāāāāāāāāāāāāāā 4s 175ms/step - accuracy: 0.9153 - loss: 0.2522
40/67 āāāāāāāāāāāāāāāāāāāā 4s 175ms/step - accuracy: 0.9155 - loss: 0.2516
41/67 āāāāāāāāāāāāāāāāāāāā 4s 175ms/step - accuracy: 0.9158 - loss: 0.2510
42/67 āāāāāāāāāāāāāāāāāāāā 4s 175ms/step - accuracy: 0.9161 - loss: 0.2505
43/67 āāāāāāāāāāāāāāāāāāāā 4s 175ms/step - accuracy: 0.9163 - loss: 0.2500
44/67 āāāāāāāāāāāāāāāāāāāā 4s 175ms/step - accuracy: 0.9165 - loss: 0.2495
45/67 āāāāāāāāāāāāāāāāāāāā 3s 175ms/step - accuracy: 0.9167 - loss: 0.2490
46/67 āāāāāāāāāāāāāāāāāāāā 3s 175ms/step - accuracy: 0.9169 - loss: 0.2486
47/67 āāāāāāāāāāāāāāāāāāāā 3s 175ms/step - accuracy: 0.9172 - loss: 0.2481
48/67 āāāāāāāāāāāāāāāāāāāā 3s 175ms/step - accuracy: 0.9173 - loss: 0.2477
49/67 āāāāāāāāāāāāāāāāāāāā 3s 175ms/step - accuracy: 0.9175 - loss: 0.2472
50/67 āāāāāāāāāāāāāāāāāāāā 2s 175ms/step - accuracy: 0.9178 - loss: 0.2467
51/67 āāāāāāāāāāāāāāāāāāāā 2s 175ms/step - accuracy: 0.9179 - loss: 0.2463
52/67 āāāāāāāāāāāāāāāāāāāā 2s 175ms/step - accuracy: 0.9181 - loss: 0.2459
53/67 āāāāāāāāāāāāāāāāāāāā 2s 175ms/step - accuracy: 0.9183 - loss: 0.2454
54/67 āāāāāāāāāāāāāāāāāāāā 2s 175ms/step - accuracy: 0.9185 - loss: 0.2450
55/67 āāāāāāāāāāāāāāāāāāāā 2s 175ms/step - accuracy: 0.9188 - loss: 0.2445
56/67 āāāāāāāāāāāāāāāāāāāā 1s 175ms/step - accuracy: 0.9190 - loss: 0.2441
57/67 āāāāāāāāāāāāāāāāāāāā 1s 175ms/step - accuracy: 0.9192 - loss: 0.2436
58/67 āāāāāāāāāāāāāāāāāāāā 1s 175ms/step - accuracy: 0.9194 - loss: 0.2431
59/67 āāāāāāāāāāāāāāāāāāāā 1s 175ms/step - accuracy: 0.9196 - loss: 0.2427
60/67 āāāāāāāāāāāāāāāāāāāā 1s 175ms/step - accuracy: 0.9199 - loss: 0.2422
61/67 āāāāāāāāāāāāāāāāāāāā 1s 175ms/step - accuracy: 0.9201 - loss: 0.2418
62/67 āāāāāāāāāāāāāāāāāāāā 0s 175ms/step - accuracy: 0.9202 - loss: 0.2414
63/67 āāāāāāāāāāāāāāāāāāāā 0s 175ms/step - accuracy: 0.9204 - loss: 0.2410
64/67 āāāāāāāāāāāāāāāāāāāā 0s 175ms/step - accuracy: 0.9206 - loss: 0.2405
65/67 āāāāāāāāāāāāāāāāāāāā 0s 175ms/step - accuracy: 0.9207 - loss: 0.2401
66/67 āāāāāāāāāāāāāāāāāāāā 0s 175ms/step - accuracy: 0.9209 - loss: 0.2397
67/67 āāāāāāāāāāāāāāāāāāāā 0s 175ms/step - accuracy: 0.9210 - loss: 0.2394
67/67 āāāāāāāāāāāāāāāāāāāā 13s 190ms/step - accuracy: 0.9212 - loss: 0.2391 - val_accuracy: 0.8950 - val_loss: 0.2628
Epoch 5/5
1/67 āāāāāāāāāāāāāāāāāāāā 11s 180ms/step - accuracy: 0.9375 - loss: 0.1780
2/67 āāāāāāāāāāāāāāāāāāāā 11s 174ms/step - accuracy: 0.9297 - loss: 0.1885
3/67 āāāāāāāāāāāāāāāāāāāā 10s 171ms/step - accuracy: 0.9323 - loss: 0.1848
4/67 āāāāāāāāāāāāāāāāāāāā 10s 173ms/step - accuracy: 0.9336 - loss: 0.1816
5/67 āāāāāāāāāāāāāāāāāāāā 10s 173ms/step - accuracy: 0.9369 - loss: 0.1771
6/67 āāāāāāāāāāāāāāāāāāāā 10s 173ms/step - accuracy: 0.9396 - loss: 0.1735
7/67 āāāāāāāāāāāāāāāāāāāā 10s 174ms/step - accuracy: 0.9412 - loss: 0.1702
8/67 āāāāāāāāāāāāāāāāāāāā 10s 174ms/step - accuracy: 0.9432 - loss: 0.1673
9/67 āāāāāāāāāāāāāāāāāāāā 10s 174ms/step - accuracy: 0.9449 - loss: 0.1646
10/67 āāāāāāāāāāāāāāāāāāāā 9s 174ms/step - accuracy: 0.9457 - loss: 0.1633
11/67 āāāāāāāāāāāāāāāāāāāā 9s 174ms/step - accuracy: 0.9462 - loss: 0.1624
12/67 āāāāāāāāāāāāāāāāāāāā 9s 174ms/step - accuracy: 0.9466 - loss: 0.1619
13/67 āāāāāāāāāāāāāāāāāāāā 9s 174ms/step - accuracy: 0.9468 - loss: 0.1617
14/67 āāāāāāāāāāāāāāāāāāāā 9s 174ms/step - accuracy: 0.9471 - loss: 0.1614
15/67 āāāāāāāāāāāāāāāāāāāā 9s 174ms/step - accuracy: 0.9473 - loss: 0.1612
16/67 āāāāāāāāāāāāāāāāāāāā 8s 174ms/step - accuracy: 0.9475 - loss: 0.1609
17/67 āāāāāāāāāāāāāāāāāāāā 8s 174ms/step - accuracy: 0.9477 - loss: 0.1606
18/67 āāāāāāāāāāāāāāāāāāāā 8s 174ms/step - accuracy: 0.9480 - loss: 0.1601
19/67 āāāāāāāāāāāāāāāāāāāā 8s 174ms/step - accuracy: 0.9484 - loss: 0.1594
20/67 āāāāāāāāāāāāāāāāāāāā 8s 174ms/step - accuracy: 0.9487 - loss: 0.1588
21/67 āāāāāāāāāāāāāāāāāāāā 8s 174ms/step - accuracy: 0.9491 - loss: 0.1581
22/67 āāāāāāāāāāāāāāāāāāāā 7s 175ms/step - accuracy: 0.9496 - loss: 0.1573
23/67 āāāāāāāāāāāāāāāāāāāā 7s 175ms/step - accuracy: 0.9500 - loss: 0.1566
24/67 āāāāāāāāāāāāāāāāāāāā 7s 175ms/step - accuracy: 0.9503 - loss: 0.1561
25/67 āāāāāāāāāāāāāāāāāāāā 7s 175ms/step - accuracy: 0.9507 - loss: 0.1556
26/67 āāāāāāāāāāāāāāāāāāāā 7s 175ms/step - accuracy: 0.9510 - loss: 0.1550
27/67 āāāāāāāāāāāāāāāāāāāā 7s 175ms/step - accuracy: 0.9513 - loss: 0.1545
28/67 āāāāāāāāāāāāāāāāāāāā 6s 175ms/step - accuracy: 0.9515 - loss: 0.1540
29/67 āāāāāāāāāāāāāāāāāāāā 6s 175ms/step - accuracy: 0.9518 - loss: 0.1535
30/67 āāāāāāāāāāāāāāāāāāāā 6s 175ms/step - accuracy: 0.9520 - loss: 0.1530
31/67 āāāāāāāāāāāāāāāāāāāā 6s 175ms/step - accuracy: 0.9523 - loss: 0.1525
32/67 āāāāāāāāāāāāāāāāāāāā 6s 175ms/step - accuracy: 0.9524 - loss: 0.1521
33/67 āāāāāāāāāāāāāāāāāāāā 5s 175ms/step - accuracy: 0.9526 - loss: 0.1518
34/67 āāāāāāāāāāāāāāāāāāāā 5s 175ms/step - accuracy: 0.9527 - loss: 0.1514
35/67 āāāāāāāāāāāāāāāāāāāā 5s 175ms/step - accuracy: 0.9529 - loss: 0.1510
36/67 āāāāāāāāāāāāāāāāāāāā 5s 175ms/step - accuracy: 0.9530 - loss: 0.1507
37/67 āāāāāāāāāāāāāāāāāāāā 5s 175ms/step - accuracy: 0.9532 - loss: 0.1503
38/67 āāāāāāāāāāāāāāāāāāāā 5s 175ms/step - accuracy: 0.9533 - loss: 0.1499
39/67 āāāāāāāāāāāāāāāāāāāā 4s 175ms/step - accuracy: 0.9534 - loss: 0.1496
40/67 āāāāāāāāāāāāāāāāāāāā 4s 175ms/step - accuracy: 0.9535 - loss: 0.1492
41/67 āāāāāāāāāāāāāāāāāāāā 4s 175ms/step - accuracy: 0.9536 - loss: 0.1489
42/67 āāāāāāāāāāāāāāāāāāāā 4s 175ms/step - accuracy: 0.9537 - loss: 0.1486
43/67 āāāāāāāāāāāāāāāāāāāā 4s 175ms/step - accuracy: 0.9538 - loss: 0.1484
44/67 āāāāāāāāāāāāāāāāāāāā 4s 175ms/step - accuracy: 0.9538 - loss: 0.1481
45/67 āāāāāāāāāāāāāāāāāāāā 3s 175ms/step - accuracy: 0.9539 - loss: 0.1479
46/67 āāāāāāāāāāāāāāāāāāāā 3s 175ms/step - accuracy: 0.9539 - loss: 0.1477
47/67 āāāāāāāāāāāāāāāāāāāā 3s 175ms/step - accuracy: 0.9540 - loss: 0.1474
48/67 āāāāāāāāāāāāāāāāāāāā 3s 175ms/step - accuracy: 0.9540 - loss: 0.1472
49/67 āāāāāāāāāāāāāāāāāāāā 3s 175ms/step - accuracy: 0.9541 - loss: 0.1470
50/67 āāāāāāāāāāāāāāāāāāāā 2s 175ms/step - accuracy: 0.9541 - loss: 0.1468
51/67 āāāāāāāāāāāāāāāāāāāā 2s 175ms/step - accuracy: 0.9542 - loss: 0.1466
52/67 āāāāāāāāāāāāāāāāāāāā 2s 175ms/step - accuracy: 0.9542 - loss: 0.1464
53/67 āāāāāāāāāāāāāāāāāāāā 2s 175ms/step - accuracy: 0.9543 - loss: 0.1462
54/67 āāāāāāāāāāāāāāāāāāāā 2s 175ms/step - accuracy: 0.9543 - loss: 0.1461
55/67 āāāāāāāāāāāāāāāāāāāā 2s 175ms/step - accuracy: 0.9544 - loss: 0.1459
56/67 āāāāāāāāāāāāāāāāāāāā 1s 175ms/step - accuracy: 0.9545 - loss: 0.1458
57/67 āāāāāāāāāāāāāāāāāāāā 1s 175ms/step - accuracy: 0.9545 - loss: 0.1456
58/67 āāāāāāāāāāāāāāāāāāāā 1s 175ms/step - accuracy: 0.9546 - loss: 0.1455
59/67 āāāāāāāāāāāāāāāāāāāā 1s 175ms/step - accuracy: 0.9546 - loss: 0.1453
60/67 āāāāāāāāāāāāāāāāāāāā 1s 175ms/step - accuracy: 0.9546 - loss: 0.1451
61/67 āāāāāāāāāāāāāāāāāāāā 1s 175ms/step - accuracy: 0.9547 - loss: 0.1450
62/67 āāāāāāāāāāāāāāāāāāāā 0s 175ms/step - accuracy: 0.9548 - loss: 0.1448
63/67 āāāāāāāāāāāāāāāāāāāā 0s 175ms/step - accuracy: 0.9548 - loss: 0.1446
64/67 āāāāāāāāāāāāāāāāāāāā 0s 175ms/step - accuracy: 0.9549 - loss: 0.1445
65/67 āāāāāāāāāāāāāāāāāāāā 0s 175ms/step - accuracy: 0.9549 - loss: 0.1443
66/67 āāāāāāāāāāāāāāāāāāāā 0s 175ms/step - accuracy: 0.9550 - loss: 0.1442
67/67 āāāāāāāāāāāāāāāāāāāā 0s 175ms/step - accuracy: 0.9550 - loss: 0.1440
67/67 āāāāāāāāāāāāāāāāāāāā 13s 189ms/step - accuracy: 0.9551 - loss: 0.1438 - val_accuracy: 0.8610 - val_loss: 0.3753
1/23 āāāāāāāāāāāāāāāāāāāā 2s 127ms/step
3/23 āāāāāāāāāāāāāāāāāāāā 0s 42ms/step
5/23 āāāāāāāāāāāāāāāāāāāā 0s 45ms/step
7/23 āāāāāāāāāāāāāāāāāāāā 0s 44ms/step
9/23 āāāāāāāāāāāāāāāāāāāā 0s 44ms/step
11/23 āāāāāāāāāāāāāāāāāāāā 0s 44ms/step
13/23 āāāāāāāāāāāāāāāāāāāā 0s 43ms/step
15/23 āāāāāāāāāāāāāāāāāāāā 0s 43ms/step
17/23 āāāāāāāāāāāāāāāāāāāā 0s 42ms/step
19/23 āāāāāāāāāāāāāāāāāāāā 0s 43ms/step
21/23 āāāāāāāāāāāāāāāāāāāā 0s 43ms/step
23/23 āāāāāāāāāāāāāāāāāāāā 0s 45ms/step
23/23 āāāāāāāāāāāāāāāāāāāā 1s 45ms/step
Accuracy: 0.8609929078014185 Precision: 0.9411764705882353 Recall: 0.7705382436260623 Epoch 1/5
1/53 āāāāāāāāāāāāāāāāāāāā 53s 1s/step - accuracy: 0.3125 - loss: 0.6945
2/53 āāāāāāāāāāāāāāāāāāāā 4s 82ms/step - accuracy: 0.3750 - loss: 0.6940
3/53 āāāāāāāāāāāāāāāāāāāā 3s 80ms/step - accuracy: 0.4132 - loss: 0.6937
4/53 āāāāāāāāāāāāāāāāāāāā 3s 80ms/step - accuracy: 0.4388 - loss: 0.6934
5/53 āāāāāāāāāāāāāāāāāāāā 3s 80ms/step - accuracy: 0.4598 - loss: 0.6930
6/53 āāāāāāāāāāāāāāāāāāāā 3s 79ms/step - accuracy: 0.4778 - loss: 0.6927
7/53 āāāāāāāāāāāāāāāāāāāā 3s 79ms/step - accuracy: 0.4950 - loss: 0.6922
8/53 āāāāāāāāāāāāāāāāāāāā 3s 79ms/step - accuracy: 0.5073 - loss: 0.6918
9/53 āāāāāāāāāāāāāāāāāāāā 3s 79ms/step - accuracy: 0.5204 - loss: 0.6913
10/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.5309 - loss: 0.6909
11/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.5410 - loss: 0.6904
12/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.5486 - loss: 0.6900
13/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.5558 - loss: 0.6896
14/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.5623 - loss: 0.6891
15/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.5687 - loss: 0.6886
16/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.5748 - loss: 0.6881
17/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.5803 - loss: 0.6875
18/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.5851 - loss: 0.6869
19/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.5893 - loss: 0.6862
20/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.5930 - loss: 0.6857
21/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.5961 - loss: 0.6852
22/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.5992 - loss: 0.6848
23/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.6023 - loss: 0.6843
24/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.6053 - loss: 0.6838
25/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.6082 - loss: 0.6832
26/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.6108 - loss: 0.6826
27/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.6132 - loss: 0.6820
28/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.6152 - loss: 0.6814
29/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.6172 - loss: 0.6807
30/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.6192 - loss: 0.6800
31/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.6211 - loss: 0.6792
32/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.6230 - loss: 0.6783
33/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.6247 - loss: 0.6774
34/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.6264 - loss: 0.6766
35/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.6279 - loss: 0.6758
36/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.6294 - loss: 0.6749
37/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.6307 - loss: 0.6741
38/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.6320 - loss: 0.6733
39/53 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step - accuracy: 0.6332 - loss: 0.6725
40/53 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step - accuracy: 0.6344 - loss: 0.6717
41/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.6355 - loss: 0.6709
42/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.6366 - loss: 0.6702
43/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.6376 - loss: 0.6694
44/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.6386 - loss: 0.6686
45/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.6395 - loss: 0.6678
46/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.6403 - loss: 0.6671
47/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.6411 - loss: 0.6664
48/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.6418 - loss: 0.6657
49/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.6426 - loss: 0.6649
50/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.6432 - loss: 0.6643
51/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.6439 - loss: 0.6636
52/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.6445 - loss: 0.6629
53/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.6451 - loss: 0.6621
53/53 āāāāāāāāāāāāāāāāāāāā 6s 89ms/step - accuracy: 0.6458 - loss: 0.6614 - val_accuracy: 0.7245 - val_loss: 0.5379
Epoch 2/5
1/53 āāāāāāāāāāāāāāāāāāāā 4s 88ms/step - accuracy: 0.7188 - loss: 0.5715
2/53 āāāāāāāāāāāāāāāāāāāā 4s 78ms/step - accuracy: 0.7266 - loss: 0.5501
3/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.7483 - loss: 0.5366
4/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.7546 - loss: 0.5304
5/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.7599 - loss: 0.5284
6/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.7643 - loss: 0.5276
7/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.7699 - loss: 0.5245
8/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.7757 - loss: 0.5209
9/53 āāāāāāāāāāāāāāāāāāāā 3s 79ms/step - accuracy: 0.7794 - loss: 0.5182
10/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.7815 - loss: 0.5157
11/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.7830 - loss: 0.5134
12/53 āāāāāāāāāāāāāāāāāāāā 3s 79ms/step - accuracy: 0.7840 - loss: 0.5117
13/53 āāāāāāāāāāāāāāāāāāāā 3s 79ms/step - accuracy: 0.7847 - loss: 0.5101
14/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.7852 - loss: 0.5087
15/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.7855 - loss: 0.5075
16/53 āāāāāāāāāāāāāāāāāāāā 2s 79ms/step - accuracy: 0.7862 - loss: 0.5058
17/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.7867 - loss: 0.5043
18/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.7869 - loss: 0.5033
19/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.7869 - loss: 0.5025
20/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.7871 - loss: 0.5013
21/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.7874 - loss: 0.4998
22/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.7876 - loss: 0.4984
23/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.7876 - loss: 0.4971
24/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.7875 - loss: 0.4961
25/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.7872 - loss: 0.4951
26/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.7870 - loss: 0.4941
27/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.7868 - loss: 0.4929
28/53 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step - accuracy: 0.7868 - loss: 0.4919
29/53 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step - accuracy: 0.7869 - loss: 0.4908
30/53 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step - accuracy: 0.7871 - loss: 0.4897
31/53 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step - accuracy: 0.7873 - loss: 0.4886
32/53 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step - accuracy: 0.7876 - loss: 0.4875
33/53 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step - accuracy: 0.7878 - loss: 0.4865
34/53 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step - accuracy: 0.7879 - loss: 0.4855
35/53 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step - accuracy: 0.7882 - loss: 0.4845
36/53 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step - accuracy: 0.7884 - loss: 0.4835
37/53 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step - accuracy: 0.7889 - loss: 0.4824
38/53 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step - accuracy: 0.7893 - loss: 0.4813
39/53 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step - accuracy: 0.7897 - loss: 0.4802
40/53 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step - accuracy: 0.7902 - loss: 0.4792
41/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.7906 - loss: 0.4783
42/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.7910 - loss: 0.4773
43/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.7915 - loss: 0.4765
44/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.7919 - loss: 0.4756
45/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.7924 - loss: 0.4748
46/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.7928 - loss: 0.4740
47/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.7932 - loss: 0.4732
48/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.7936 - loss: 0.4723
49/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.7941 - loss: 0.4715
50/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.7945 - loss: 0.4706
51/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.7948 - loss: 0.4701
52/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.7951 - loss: 0.4697
53/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.7953 - loss: 0.4693
53/53 āāāāāāāāāāāāāāāāāāāā 5s 86ms/step - accuracy: 0.7955 - loss: 0.4689 - val_accuracy: 0.8193 - val_loss: 0.4126
Epoch 3/5
1/53 āāāāāāāāāāāāāāāāāāāā 4s 88ms/step - accuracy: 0.8125 - loss: 0.4465
2/53 āāāāāāāāāāāāāāāāāāāā 4s 82ms/step - accuracy: 0.8281 - loss: 0.4266
3/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.8472 - loss: 0.4059
4/53 āāāāāāāāāāāāāāāāāāāā 3s 79ms/step - accuracy: 0.8542 - loss: 0.3942
5/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.8558 - loss: 0.3929
6/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.8556 - loss: 0.3924
7/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.8558 - loss: 0.3909
8/53 āāāāāāāāāāāāāāāāāāāā 3s 77ms/step - accuracy: 0.8557 - loss: 0.3889
9/53 āāāāāāāāāāāāāāāāāāāā 3s 77ms/step - accuracy: 0.8556 - loss: 0.3882
10/53 āāāāāāāāāāāāāāāāāāāā 3s 77ms/step - accuracy: 0.8556 - loss: 0.3884
11/53 āāāāāāāāāāāāāāāāāāāā 3s 77ms/step - accuracy: 0.8558 - loss: 0.3884
12/53 āāāāāāāāāāāāāāāāāāāā 3s 76ms/step - accuracy: 0.8555 - loss: 0.3891
13/53 āāāāāāāāāāāāāāāāāāāā 3s 76ms/step - accuracy: 0.8553 - loss: 0.3896
14/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.8553 - loss: 0.3901
15/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.8552 - loss: 0.3904
16/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.8556 - loss: 0.3901
17/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.8560 - loss: 0.3899
18/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.8562 - loss: 0.3900
19/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.8562 - loss: 0.3904
20/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.8565 - loss: 0.3905
21/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.8568 - loss: 0.3907
22/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.8571 - loss: 0.3907
23/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.8573 - loss: 0.3907
24/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.8575 - loss: 0.3908
25/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.8577 - loss: 0.3908
26/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.8579 - loss: 0.3906
27/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.8580 - loss: 0.3906
28/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.8581 - loss: 0.3905
29/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.8583 - loss: 0.3904
30/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.8585 - loss: 0.3902
31/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.8587 - loss: 0.3900
32/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.8589 - loss: 0.3898
33/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.8591 - loss: 0.3896
34/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.8593 - loss: 0.3893
35/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.8595 - loss: 0.3892
36/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.8596 - loss: 0.3890
37/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.8598 - loss: 0.3888
38/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.8600 - loss: 0.3886
39/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.8602 - loss: 0.3884
40/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.8605 - loss: 0.3881
41/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.8607 - loss: 0.3878
42/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.8609 - loss: 0.3876
43/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.8610 - loss: 0.3873
44/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.8613 - loss: 0.3870
45/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.8615 - loss: 0.3866
46/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.8617 - loss: 0.3862
47/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.8619 - loss: 0.3859
48/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.8621 - loss: 0.3855
49/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.8624 - loss: 0.3851
50/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.8626 - loss: 0.3847
51/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.8628 - loss: 0.3843
52/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.8631 - loss: 0.3839
53/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.8633 - loss: 0.3835
53/53 āāāāāāāāāāāāāāāāāāāā 5s 85ms/step - accuracy: 0.8635 - loss: 0.3831 - val_accuracy: 0.8318 - val_loss: 0.3861
Epoch 4/5
1/53 āāāāāāāāāāāāāāāāāāāā 4s 86ms/step - accuracy: 0.9375 - loss: 0.2007
2/53 āāāāāāāāāāāāāāāāāāāā 3s 77ms/step - accuracy: 0.9531 - loss: 0.1837
3/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.9340 - loss: 0.2071
4/53 āāāāāāāāāāāāāāāāāāāā 3s 79ms/step - accuracy: 0.9271 - loss: 0.2159
5/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.9217 - loss: 0.2237
6/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.9208 - loss: 0.2259
7/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.9200 - loss: 0.2303
8/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.9188 - loss: 0.2342
9/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.9182 - loss: 0.2365
10/53 āāāāāāāāāāāāāāāāāāāā 3s 77ms/step - accuracy: 0.9185 - loss: 0.2372
11/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.9190 - loss: 0.2375
12/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.9194 - loss: 0.2376
13/53 āāāāāāāāāāāāāāāāāāāā 3s 78ms/step - accuracy: 0.9199 - loss: 0.2377
14/53 āāāāāāāāāāāāāāāāāāāā 3s 77ms/step - accuracy: 0.9202 - loss: 0.2380
15/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9202 - loss: 0.2384
16/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9203 - loss: 0.2385
17/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9206 - loss: 0.2382
18/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9209 - loss: 0.2378
19/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9210 - loss: 0.2374
20/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9210 - loss: 0.2374
21/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9210 - loss: 0.2373
22/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9212 - loss: 0.2369
23/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9213 - loss: 0.2366
24/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9214 - loss: 0.2364
25/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9215 - loss: 0.2361
26/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9216 - loss: 0.2357
27/53 āāāāāāāāāāāāāāāāāāāā 2s 78ms/step - accuracy: 0.9217 - loss: 0.2353
28/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9217 - loss: 0.2350
29/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9218 - loss: 0.2346
30/53 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step - accuracy: 0.9218 - loss: 0.2342
31/53 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step - accuracy: 0.9219 - loss: 0.2338
32/53 āāāāāāāāāāāāāāāāāāāā 1s 78ms/step - accuracy: 0.9219 - loss: 0.2333
33/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9219 - loss: 0.2329
34/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9220 - loss: 0.2324
35/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9222 - loss: 0.2318
36/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9223 - loss: 0.2313
37/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9224 - loss: 0.2308
38/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9226 - loss: 0.2303
39/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9227 - loss: 0.2298
40/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9228 - loss: 0.2294
41/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9229 - loss: 0.2290
42/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9229 - loss: 0.2287
43/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9229 - loss: 0.2284
44/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9230 - loss: 0.2281
45/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9230 - loss: 0.2278
46/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9230 - loss: 0.2276
47/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9231 - loss: 0.2273
48/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9232 - loss: 0.2269
49/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.9233 - loss: 0.2266
50/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.9233 - loss: 0.2263
51/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.9234 - loss: 0.2260
52/53 āāāāāāāāāāāāāāāāāāāā 0s 78ms/step - accuracy: 0.9234 - loss: 0.2257
53/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9235 - loss: 0.2254
53/53 āāāāāāāāāāāāāāāāāāāā 5s 86ms/step - accuracy: 0.9236 - loss: 0.2251 - val_accuracy: 0.8658 - val_loss: 0.3163
Epoch 5/5
1/53 āāāāāāāāāāāāāāāāāāāā 4s 85ms/step - accuracy: 0.9375 - loss: 0.2131
2/53 āāāāāāāāāāāāāāāāāāāā 3s 74ms/step - accuracy: 0.9531 - loss: 0.1843
3/53 āāāāāāāāāāāāāāāāāāāā 3s 75ms/step - accuracy: 0.9618 - loss: 0.1641
4/53 āāāāāāāāāāāāāāāāāāāā 3s 75ms/step - accuracy: 0.9655 - loss: 0.1551
5/53 āāāāāāāāāāāāāāāāāāāā 3s 75ms/step - accuracy: 0.9649 - loss: 0.1532
6/53 āāāāāāāāāāāāāāāāāāāā 3s 76ms/step - accuracy: 0.9629 - loss: 0.1533
7/53 āāāāāāāāāāāāāāāāāāāā 3s 77ms/step - accuracy: 0.9619 - loss: 0.1525
8/53 āāāāāāāāāāāāāāāāāāāā 3s 77ms/step - accuracy: 0.9612 - loss: 0.1517
9/53 āāāāāāāāāāāāāāāāāāāā 3s 77ms/step - accuracy: 0.9605 - loss: 0.1516
10/53 āāāāāāāāāāāāāāāāāāāā 3s 77ms/step - accuracy: 0.9604 - loss: 0.1511
11/53 āāāāāāāāāāāāāāāāāāāā 3s 77ms/step - accuracy: 0.9594 - loss: 0.1523
12/53 āāāāāāāāāāāāāāāāāāāā 3s 77ms/step - accuracy: 0.9589 - loss: 0.1528
13/53 āāāāāāāāāāāāāāāāāāāā 3s 77ms/step - accuracy: 0.9585 - loss: 0.1532
14/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9581 - loss: 0.1533
15/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9580 - loss: 0.1531
16/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9579 - loss: 0.1528
17/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9580 - loss: 0.1522
18/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9582 - loss: 0.1517
19/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9584 - loss: 0.1511
20/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9586 - loss: 0.1506
21/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9587 - loss: 0.1500
22/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9588 - loss: 0.1495
23/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9589 - loss: 0.1490
24/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9589 - loss: 0.1486
25/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9589 - loss: 0.1484
26/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9588 - loss: 0.1481
27/53 āāāāāāāāāāāāāāāāāāāā 2s 77ms/step - accuracy: 0.9587 - loss: 0.1479
28/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9585 - loss: 0.1477
29/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9583 - loss: 0.1477
30/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9581 - loss: 0.1476
31/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9579 - loss: 0.1476
32/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9576 - loss: 0.1477
33/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9574 - loss: 0.1477
34/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9573 - loss: 0.1476
35/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9571 - loss: 0.1475
36/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9570 - loss: 0.1473
37/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9570 - loss: 0.1471
38/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9569 - loss: 0.1469
39/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9569 - loss: 0.1467
40/53 āāāāāāāāāāāāāāāāāāāā 1s 77ms/step - accuracy: 0.9568 - loss: 0.1465
41/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9568 - loss: 0.1462
42/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9567 - loss: 0.1461
43/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9567 - loss: 0.1459
44/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9566 - loss: 0.1458
45/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9565 - loss: 0.1456
46/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9564 - loss: 0.1455
47/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9563 - loss: 0.1453
48/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9562 - loss: 0.1452
49/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9562 - loss: 0.1451
50/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9561 - loss: 0.1449
51/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9560 - loss: 0.1448
52/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9559 - loss: 0.1447
53/53 āāāāāāāāāāāāāāāāāāāā 0s 77ms/step - accuracy: 0.9557 - loss: 0.1446
53/53 āāāāāāāāāāāāāāāāāāāā 5s 86ms/step - accuracy: 0.9556 - loss: 0.1445 - val_accuracy: 0.8587 - val_loss: 0.3177
1/18 āāāāāāāāāāāāāāāāāāāā 1s 99ms/step
4/18 āāāāāāāāāāāāāāāāāāāā 0s 23ms/step
7/18 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step
10/18 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step
12/18 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step
15/18 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step
18/18 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step
18/18 āāāāāāāāāāāāāāāāāāāā 1s 28ms/step
Accuracy: 0.8586762075134168 Precision: 0.8853754940711462 Recall: 0.8175182481751825 Epoch 1/5
1/48 āāāāāāāāāāāāāāāāāāāā 53s 1s/step - accuracy: 0.4688 - loss: 0.6935
2/48 āāāāāāāāāāāāāāāāāāāā 9s 200ms/step - accuracy: 0.4766 - loss: 0.6935
3/48 āāāāāāāāāāāāāāāāāāāā 8s 200ms/step - accuracy: 0.4670 - loss: 0.6935
4/48 āāāāāāāāāāāāāāāāāāāā 8s 200ms/step - accuracy: 0.4753 - loss: 0.6934
5/48 āāāāāāāāāāāāāāāāāāāā 8s 199ms/step - accuracy: 0.4815 - loss: 0.6934
6/48 āāāāāāāāāāāāāāāāāāāā 8s 199ms/step - accuracy: 0.4880 - loss: 0.6933
7/48 āāāāāāāāāāāāāāāāāāāā 8s 198ms/step - accuracy: 0.4948 - loss: 0.6933
8/48 āāāāāāāāāāāāāāāāāāāā 7s 198ms/step - accuracy: 0.4989 - loss: 0.6932
9/48 āāāāāāāāāāāāāāāāāāāā 7s 197ms/step - accuracy: 0.5009 - loss: 0.6932
10/48 āāāāāāāāāāāāāāāāāāāā 7s 196ms/step - accuracy: 0.5027 - loss: 0.6932
11/48 āāāāāāāāāāāāāāāāāāāā 7s 196ms/step - accuracy: 0.5051 - loss: 0.6931
12/48 āāāāāāāāāāāāāāāāāāāā 7s 196ms/step - accuracy: 0.5075 - loss: 0.6931
13/48 āāāāāāāāāāāāāāāāāāāā 6s 197ms/step - accuracy: 0.5106 - loss: 0.6930
14/48 āāāāāāāāāāāāāāāāāāāā 6s 197ms/step - accuracy: 0.5140 - loss: 0.6930
15/48 āāāāāāāāāāāāāāāāāāāā 6s 197ms/step - accuracy: 0.5172 - loss: 0.6929
16/48 āāāāāāāāāāāāāāāāāāāā 6s 197ms/step - accuracy: 0.5202 - loss: 0.6928
17/48 āāāāāāāāāāāāāāāāāāāā 6s 197ms/step - accuracy: 0.5228 - loss: 0.6928
18/48 āāāāāāāāāāāāāāāāāāāā 5s 196ms/step - accuracy: 0.5249 - loss: 0.6927
19/48 āāāāāāāāāāāāāāāāāāāā 5s 196ms/step - accuracy: 0.5267 - loss: 0.6926
20/48 āāāāāāāāāāāāāāāāāāāā 5s 197ms/step - accuracy: 0.5285 - loss: 0.6926
21/48 āāāāāāāāāāāāāāāāāāāā 5s 196ms/step - accuracy: 0.5304 - loss: 0.6925
22/48 āāāāāāāāāāāāāāāāāāāā 5s 196ms/step - accuracy: 0.5323 - loss: 0.6925
23/48 āāāāāāāāāāāāāāāāāāāā 4s 196ms/step - accuracy: 0.5338 - loss: 0.6924
24/48 āāāāāāāāāāāāāāāāāāāā 4s 196ms/step - accuracy: 0.5349 - loss: 0.6924
25/48 āāāāāāāāāāāāāāāāāāāā 4s 196ms/step - accuracy: 0.5360 - loss: 0.6924
26/48 āāāāāāāāāāāāāāāāāāāā 4s 196ms/step - accuracy: 0.5370 - loss: 0.6923
27/48 āāāāāāāāāāāāāāāāāāāā 4s 196ms/step - accuracy: 0.5381 - loss: 0.6923
28/48 āāāāāāāāāāāāāāāāāāāā 3s 196ms/step - accuracy: 0.5393 - loss: 0.6922
29/48 āāāāāāāāāāāāāāāāāāāā 3s 196ms/step - accuracy: 0.5403 - loss: 0.6922
30/48 āāāāāāāāāāāāāāāāāāāā 3s 196ms/step - accuracy: 0.5412 - loss: 0.6921
31/48 āāāāāāāāāāāāāāāāāāāā 3s 196ms/step - accuracy: 0.5422 - loss: 0.6920
32/48 āāāāāāāāāāāāāāāāāāāā 3s 196ms/step - accuracy: 0.5432 - loss: 0.6920
33/48 āāāāāāāāāāāāāāāāāāāā 2s 196ms/step - accuracy: 0.5441 - loss: 0.6919
34/48 āāāāāāāāāāāāāāāāāāāā 2s 196ms/step - accuracy: 0.5451 - loss: 0.6919
35/48 āāāāāāāāāāāāāāāāāāāā 2s 196ms/step - accuracy: 0.5460 - loss: 0.6918
36/48 āāāāāāāāāāāāāāāāāāāā 2s 196ms/step - accuracy: 0.5470 - loss: 0.6917
37/48 āāāāāāāāāāāāāāāāāāāā 2s 196ms/step - accuracy: 0.5479 - loss: 0.6917
38/48 āāāāāāāāāāāāāāāāāāāā 1s 196ms/step - accuracy: 0.5488 - loss: 0.6916
39/48 āāāāāāāāāāāāāāāāāāāā 1s 196ms/step - accuracy: 0.5497 - loss: 0.6915
40/48 āāāāāāāāāāāāāāāāāāāā 1s 196ms/step - accuracy: 0.5505 - loss: 0.6915
41/48 āāāāāāāāāāāāāāāāāāāā 1s 196ms/step - accuracy: 0.5514 - loss: 0.6914
42/48 āāāāāāāāāāāāāāāāāāāā 1s 195ms/step - accuracy: 0.5523 - loss: 0.6913
43/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.5531 - loss: 0.6912
44/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.5540 - loss: 0.6911
45/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.5549 - loss: 0.6910
46/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.5557 - loss: 0.6910
47/48 āāāāāāāāāāāāāāāāāāāā 0s 196ms/step - accuracy: 0.5566 - loss: 0.6908
48/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.5575 - loss: 0.6907
48/48 āāāāāāāāāāāāāāāāāāāā 11s 214ms/step - accuracy: 0.5583 - loss: 0.6907 - val_accuracy: 0.6548 - val_loss: 0.6661
Epoch 2/5
1/48 āāāāāāāāāāāāāāāāāāāā 9s 207ms/step - accuracy: 0.6250 - loss: 0.6634
2/48 āāāāāāāāāāāāāāāāāāāā 9s 203ms/step - accuracy: 0.6797 - loss: 0.6584
3/48 āāāāāāāāāāāāāāāāāāāā 9s 204ms/step - accuracy: 0.7170 - loss: 0.6553
4/48 āāāāāāāāāāāāāāāāāāāā 8s 198ms/step - accuracy: 0.7311 - loss: 0.6530
5/48 āāāāāāāāāāāāāāāāāāāā 8s 196ms/step - accuracy: 0.7386 - loss: 0.6513
6/48 āāāāāāāāāāāāāāāāāāāā 8s 195ms/step - accuracy: 0.7457 - loss: 0.6495
7/48 āāāāāāāāāāāāāāāāāāāā 7s 194ms/step - accuracy: 0.7464 - loss: 0.6490
8/48 āāāāāāāāāāāāāāāāāāāā 7s 195ms/step - accuracy: 0.7444 - loss: 0.6485
9/48 āāāāāāāāāāāāāāāāāāāā 7s 194ms/step - accuracy: 0.7400 - loss: 0.6484
10/48 āāāāāāāāāāāāāāāāāāāā 7s 194ms/step - accuracy: 0.7347 - loss: 0.6486
11/48 āāāāāāāāāāāāāāāāāāāā 7s 195ms/step - accuracy: 0.7310 - loss: 0.6485
12/48 āāāāāāāāāāāāāāāāāāāā 7s 195ms/step - accuracy: 0.7286 - loss: 0.6482
13/48 āāāāāāāāāāāāāāāāāāāā 6s 196ms/step - accuracy: 0.7268 - loss: 0.6477
14/48 āāāāāāāāāāāāāāāāāāāā 6s 196ms/step - accuracy: 0.7251 - loss: 0.6471
15/48 āāāāāāāāāāāāāāāāāāāā 6s 197ms/step - accuracy: 0.7244 - loss: 0.6464
16/48 āāāāāāāāāāāāāāāāāāāā 6s 196ms/step - accuracy: 0.7240 - loss: 0.6457
17/48 āāāāāāāāāāāāāāāāāāāā 6s 196ms/step - accuracy: 0.7233 - loss: 0.6450
18/48 āāāāāāāāāāāāāāāāāāāā 5s 196ms/step - accuracy: 0.7222 - loss: 0.6445
19/48 āāāāāāāāāāāāāāāāāāāā 5s 196ms/step - accuracy: 0.7211 - loss: 0.6440
20/48 āāāāāāāāāāāāāāāāāāāā 5s 196ms/step - accuracy: 0.7202 - loss: 0.6434
21/48 āāāāāāāāāāāāāāāāāāāā 5s 196ms/step - accuracy: 0.7192 - loss: 0.6428
22/48 āāāāāāāāāāāāāāāāāāāā 5s 196ms/step - accuracy: 0.7184 - loss: 0.6421
23/48 āāāāāāāāāāāāāāāāāāāā 4s 195ms/step - accuracy: 0.7178 - loss: 0.6413
24/48 āāāāāāāāāāāāāāāāāāāā 4s 195ms/step - accuracy: 0.7174 - loss: 0.6404
25/48 āāāāāāāāāāāāāāāāāāāā 4s 195ms/step - accuracy: 0.7173 - loss: 0.6394
26/48 āāāāāāāāāāāāāāāāāāāā 4s 195ms/step - accuracy: 0.7174 - loss: 0.6383
27/48 āāāāāāāāāāāāāāāāāāāā 4s 195ms/step - accuracy: 0.7172 - loss: 0.6373
28/48 āāāāāāāāāāāāāāāāāāāā 3s 195ms/step - accuracy: 0.7171 - loss: 0.6364
29/48 āāāāāāāāāāāāāāāāāāāā 3s 195ms/step - accuracy: 0.7170 - loss: 0.6355
30/48 āāāāāāāāāāāāāāāāāāāā 3s 195ms/step - accuracy: 0.7169 - loss: 0.6347
31/48 āāāāāāāāāāāāāāāāāāāā 3s 195ms/step - accuracy: 0.7168 - loss: 0.6339
32/48 āāāāāāāāāāāāāāāāāāāā 3s 195ms/step - accuracy: 0.7169 - loss: 0.6330
33/48 āāāāāāāāāāāāāāāāāāāā 2s 195ms/step - accuracy: 0.7169 - loss: 0.6320
34/48 āāāāāāāāāāāāāāāāāāāā 2s 195ms/step - accuracy: 0.7170 - loss: 0.6311
35/48 āāāāāāāāāāāāāāāāāāāā 2s 195ms/step - accuracy: 0.7170 - loss: 0.6302
36/48 āāāāāāāāāāāāāāāāāāāā 2s 195ms/step - accuracy: 0.7170 - loss: 0.6292
37/48 āāāāāāāāāāāāāāāāāāāā 2s 195ms/step - accuracy: 0.7171 - loss: 0.6283
38/48 āāāāāāāāāāāāāāāāāāāā 1s 195ms/step - accuracy: 0.7171 - loss: 0.6273
39/48 āāāāāāāāāāāāāāāāāāāā 1s 195ms/step - accuracy: 0.7171 - loss: 0.6264
40/48 āāāāāāāāāāāāāāāāāāāā 1s 195ms/step - accuracy: 0.7171 - loss: 0.6255
41/48 āāāāāāāāāāāāāāāāāāāā 1s 195ms/step - accuracy: 0.7172 - loss: 0.6245
42/48 āāāāāāāāāāāāāāāāāāāā 1s 195ms/step - accuracy: 0.7172 - loss: 0.6236
43/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.7173 - loss: 0.6227
44/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.7174 - loss: 0.6219
45/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.7176 - loss: 0.6210
46/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.7177 - loss: 0.6201
47/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.7178 - loss: 0.6194
48/48 āāāāāāāāāāāāāāāāāāāā 0s 194ms/step - accuracy: 0.7178 - loss: 0.6187
48/48 āāāāāāāāāāāāāāāāāāāā 10s 209ms/step - accuracy: 0.7179 - loss: 0.6180 - val_accuracy: 0.6903 - val_loss: 0.5354
Epoch 3/5
1/48 āāāāāāāāāāāāāāāāāāāā 9s 201ms/step - accuracy: 0.8125 - loss: 0.4777
2/48 āāāāāāāāāāāāāāāāāāāā 9s 198ms/step - accuracy: 0.8203 - loss: 0.4702
3/48 āāāāāāāāāāāāāāāāāāāā 8s 192ms/step - accuracy: 0.8177 - loss: 0.4653
4/48 āāāāāāāāāāāāāāāāāāāā 8s 192ms/step - accuracy: 0.8164 - loss: 0.4630
5/48 āāāāāāāāāāāāāāāāāāāā 8s 192ms/step - accuracy: 0.8194 - loss: 0.4575
6/48 āāāāāāāāāāāāāāāāāāāā 8s 193ms/step - accuracy: 0.8208 - loss: 0.4522
7/48 āāāāāāāāāāāāāāāāāāāā 7s 194ms/step - accuracy: 0.8216 - loss: 0.4493
8/48 āāāāāāāāāāāāāāāāāāāā 7s 194ms/step - accuracy: 0.8209 - loss: 0.4474
9/48 āāāāāāāāāāāāāāāāāāāā 7s 194ms/step - accuracy: 0.8207 - loss: 0.4464
10/48 āāāāāāāāāāāāāāāāāāāā 7s 194ms/step - accuracy: 0.8212 - loss: 0.4453
11/48 āāāāāāāāāāāāāāāāāāāā 7s 194ms/step - accuracy: 0.8222 - loss: 0.4435
12/48 āāāāāāāāāāāāāāāāāāāā 6s 194ms/step - accuracy: 0.8227 - loss: 0.4422
13/48 āāāāāāāāāāāāāāāāāāāā 6s 194ms/step - accuracy: 0.8236 - loss: 0.4409
14/48 āāāāāāāāāāāāāāāāāāāā 6s 194ms/step - accuracy: 0.8244 - loss: 0.4396
15/48 āāāāāāāāāāāāāāāāāāāā 6s 194ms/step - accuracy: 0.8254 - loss: 0.4384
16/48 āāāāāāāāāāāāāāāāāāāā 6s 194ms/step - accuracy: 0.8268 - loss: 0.4371
17/48 āāāāāāāāāāāāāāāāāāāā 6s 195ms/step - accuracy: 0.8276 - loss: 0.4362
18/48 āāāāāāāāāāāāāāāāāāāā 5s 195ms/step - accuracy: 0.8282 - loss: 0.4354
19/48 āāāāāāāāāāāāāāāāāāāā 5s 195ms/step - accuracy: 0.8287 - loss: 0.4345
20/48 āāāāāāāāāāāāāāāāāāāā 5s 194ms/step - accuracy: 0.8293 - loss: 0.4337
21/48 āāāāāāāāāāāāāāāāāāāā 5s 195ms/step - accuracy: 0.8301 - loss: 0.4329
22/48 āāāāāāāāāāāāāāāāāāāā 5s 194ms/step - accuracy: 0.8308 - loss: 0.4320
23/48 āāāāāāāāāāāāāāāāāāāā 4s 194ms/step - accuracy: 0.8315 - loss: 0.4312
24/48 āāāāāāāāāāāāāāāāāāāā 4s 195ms/step - accuracy: 0.8320 - loss: 0.4304
25/48 āāāāāāāāāāāāāāāāāāāā 4s 195ms/step - accuracy: 0.8323 - loss: 0.4299
26/48 āāāāāāāāāāāāāāāāāāāā 4s 195ms/step - accuracy: 0.8328 - loss: 0.4291
27/48 āāāāāāāāāāāāāāāāāāāā 4s 195ms/step - accuracy: 0.8334 - loss: 0.4284
28/48 āāāāāāāāāāāāāāāāāāāā 3s 195ms/step - accuracy: 0.8340 - loss: 0.4276
29/48 āāāāāāāāāāāāāāāāāāāā 3s 195ms/step - accuracy: 0.8345 - loss: 0.4269
30/48 āāāāāāāāāāāāāāāāāāāā 3s 195ms/step - accuracy: 0.8350 - loss: 0.4262
31/48 āāāāāāāāāāāāāāāāāāāā 3s 195ms/step - accuracy: 0.8355 - loss: 0.4256
32/48 āāāāāāāāāāāāāāāāāāāā 3s 195ms/step - accuracy: 0.8357 - loss: 0.4250
33/48 āāāāāāāāāāāāāāāāāāāā 2s 195ms/step - accuracy: 0.8360 - loss: 0.4244
34/48 āāāāāāāāāāāāāāāāāāāā 2s 195ms/step - accuracy: 0.8362 - loss: 0.4238
35/48 āāāāāāāāāāāāāāāāāāāā 2s 195ms/step - accuracy: 0.8364 - loss: 0.4233
36/48 āāāāāāāāāāāāāāāāāāāā 2s 195ms/step - accuracy: 0.8367 - loss: 0.4227
37/48 āāāāāāāāāāāāāāāāāāāā 2s 195ms/step - accuracy: 0.8369 - loss: 0.4222
38/48 āāāāāāāāāāāāāāāāāāāā 1s 195ms/step - accuracy: 0.8371 - loss: 0.4216
39/48 āāāāāāāāāāāāāāāāāāāā 1s 195ms/step - accuracy: 0.8374 - loss: 0.4210
40/48 āāāāāāāāāāāāāāāāāāāā 1s 195ms/step - accuracy: 0.8377 - loss: 0.4205
41/48 āāāāāāāāāāāāāāāāāāāā 1s 195ms/step - accuracy: 0.8380 - loss: 0.4200
42/48 āāāāāāāāāāāāāāāāāāāā 1s 195ms/step - accuracy: 0.8382 - loss: 0.4194
43/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.8385 - loss: 0.4188
44/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.8387 - loss: 0.4183
45/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.8389 - loss: 0.4179
46/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.8390 - loss: 0.4174
47/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.8392 - loss: 0.4169
48/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.8394 - loss: 0.4164
48/48 āāāāāāāāāāāāāāāāāāāā 10s 209ms/step - accuracy: 0.8396 - loss: 0.4159 - val_accuracy: 0.7613 - val_loss: 0.4876
Epoch 4/5
1/48 āāāāāāāāāāāāāāāāāāāā 9s 207ms/step - accuracy: 0.8750 - loss: 0.3053
2/48 āāāāāāāāāāāāāāāāāāāā 8s 186ms/step - accuracy: 0.8359 - loss: 0.3364
3/48 āāāāāāāāāāāāāāāāāāāā 8s 190ms/step - accuracy: 0.8420 - loss: 0.3324
4/48 āāāāāāāāāāāāāāāāāāāā 8s 190ms/step - accuracy: 0.8522 - loss: 0.3227
5/48 āāāāāāāāāāāāāāāāāāāā 8s 190ms/step - accuracy: 0.8580 - loss: 0.3173
6/48 āāāāāāāāāāāāāāāāāāāā 8s 193ms/step - accuracy: 0.8600 - loss: 0.3165
7/48 āāāāāāāāāāāāāāāāāāāā 7s 194ms/step - accuracy: 0.8628 - loss: 0.3143
8/48 āāāāāāāāāāāāāāāāāāāā 7s 195ms/step - accuracy: 0.8653 - loss: 0.3116
9/48 āāāāāāāāāāāāāāāāāāāā 7s 194ms/step - accuracy: 0.8671 - loss: 0.3092
10/48 āāāāāāāāāāāāāāāāāāāā 7s 195ms/step - accuracy: 0.8676 - loss: 0.3079
11/48 āāāāāāāāāāāāāāāāāāāā 7s 194ms/step - accuracy: 0.8683 - loss: 0.3063
12/48 āāāāāāāāāāāāāāāāāāāā 7s 195ms/step - accuracy: 0.8697 - loss: 0.3039
13/48 āāāāāāāāāāāāāāāāāāāā 6s 194ms/step - accuracy: 0.8712 - loss: 0.3018
14/48 āāāāāāāāāāāāāāāāāāāā 6s 194ms/step - accuracy: 0.8724 - loss: 0.3001
15/48 āāāāāāāāāāāāāāāāāāāā 6s 193ms/step - accuracy: 0.8736 - loss: 0.2986
16/48 āāāāāāāāāāāāāāāāāāāā 6s 194ms/step - accuracy: 0.8749 - loss: 0.2971
17/48 āāāāāāāāāāāāāāāāāāāā 6s 194ms/step - accuracy: 0.8760 - loss: 0.2957
18/48 āāāāāāāāāāāāāāāāāāāā 5s 194ms/step - accuracy: 0.8770 - loss: 0.2948
19/48 āāāāāāāāāāāāāāāāāāāā 5s 194ms/step - accuracy: 0.8778 - loss: 0.2940
20/48 āāāāāāāāāāāāāāāāāāāā 5s 193ms/step - accuracy: 0.8787 - loss: 0.2932
21/48 āāāāāāāāāāāāāāāāāāāā 5s 193ms/step - accuracy: 0.8795 - loss: 0.2928
22/48 āāāāāāāāāāāāāāāāāāāā 5s 194ms/step - accuracy: 0.8804 - loss: 0.2924
23/48 āāāāāāāāāāāāāāāāāāāā 4s 194ms/step - accuracy: 0.8813 - loss: 0.2919
24/48 āāāāāāāāāāāāāāāāāāāā 4s 194ms/step - accuracy: 0.8821 - loss: 0.2914
25/48 āāāāāāāāāāāāāāāāāāāā 4s 194ms/step - accuracy: 0.8829 - loss: 0.2910
26/48 āāāāāāāāāāāāāāāāāāāā 4s 194ms/step - accuracy: 0.8838 - loss: 0.2904
27/48 āāāāāāāāāāāāāāāāāāāā 4s 194ms/step - accuracy: 0.8846 - loss: 0.2899
28/48 āāāāāāāāāāāāāāāāāāāā 3s 195ms/step - accuracy: 0.8853 - loss: 0.2895
29/48 āāāāāāāāāāāāāāāāāāāā 3s 195ms/step - accuracy: 0.8859 - loss: 0.2891
30/48 āāāāāāāāāāāāāāāāāāāā 3s 195ms/step - accuracy: 0.8864 - loss: 0.2889
31/48 āāāāāāāāāāāāāāāāāāāā 3s 195ms/step - accuracy: 0.8869 - loss: 0.2887
32/48 āāāāāāāāāāāāāāāāāāāā 3s 194ms/step - accuracy: 0.8873 - loss: 0.2886
33/48 āāāāāāāāāāāāāāāāāāāā 2s 194ms/step - accuracy: 0.8876 - loss: 0.2885
34/48 āāāāāāāāāāāāāāāāāāāā 2s 194ms/step - accuracy: 0.8879 - loss: 0.2885
35/48 āāāāāāāāāāāāāāāāāāāā 2s 194ms/step - accuracy: 0.8882 - loss: 0.2884
36/48 āāāāāāāāāāāāāāāāāāāā 2s 194ms/step - accuracy: 0.8885 - loss: 0.2883
37/48 āāāāāāāāāāāāāāāāāāāā 2s 194ms/step - accuracy: 0.8888 - loss: 0.2881
38/48 āāāāāāāāāāāāāāāāāāāā 1s 194ms/step - accuracy: 0.8890 - loss: 0.2880
39/48 āāāāāāāāāāāāāāāāāāāā 1s 194ms/step - accuracy: 0.8892 - loss: 0.2878
40/48 āāāāāāāāāāāāāāāāāāāā 1s 194ms/step - accuracy: 0.8893 - loss: 0.2877
41/48 āāāāāāāāāāāāāāāāāāāā 1s 195ms/step - accuracy: 0.8895 - loss: 0.2876
42/48 āāāāāāāāāāāāāāāāāāāā 1s 195ms/step - accuracy: 0.8896 - loss: 0.2874
43/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.8897 - loss: 0.2872
44/48 āāāāāāāāāāāāāāāāāāāā 0s 194ms/step - accuracy: 0.8899 - loss: 0.2870
45/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.8901 - loss: 0.2867
46/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.8902 - loss: 0.2864
47/48 āāāāāāāāāāāāāāāāāāāā 0s 195ms/step - accuracy: 0.8904 - loss: 0.2862
48/48 āāāāāāāāāāāāāāāāāāāā 0s 194ms/step - accuracy: 0.8905 - loss: 0.2859
48/48 āāāāāāāāāāāāāāāāāāāā 10s 210ms/step - accuracy: 0.8907 - loss: 0.2856 - val_accuracy: 0.7890 - val_loss: 0.4333
Epoch 5/5
1/48 āāāāāāāāāāāāāāāāāāāā 9s 207ms/step - accuracy: 0.8750 - loss: 0.1877
2/48 āāāāāāāāāāāāāāāāāāāā 8s 191ms/step - accuracy: 0.8828 - loss: 0.1769
3/48 āāāāāāāāāāāāāāāāāāāā 8s 189ms/step - accuracy: 0.8872 - loss: 0.1783
4/48 āāāāāāāāāāāāāāāāāāāā 8s 191ms/step - accuracy: 0.8880 - loss: 0.1831
5/48 āāāāāāāāāāāāāāāāāāāā 8s 192ms/step - accuracy: 0.8892 - loss: 0.1875
6/48 āāāāāāāāāāāāāāāāāāāā 8s 191ms/step - accuracy: 0.8911 - loss: 0.1891
7/48 āāāāāāāāāāāāāāāāāāāā 7s 192ms/step - accuracy: 0.8927 - loss: 0.1910
8/48 āāāāāāāāāāāāāāāāāāāā 7s 191ms/step - accuracy: 0.8924 - loss: 0.1947
9/48 āāāāāāāāāāāāāāāāāāāā 7s 192ms/step - accuracy: 0.8932 - loss: 0.1965
10/48 āāāāāāāāāāāāāāāāāāāā 7s 192ms/step - accuracy: 0.8942 - loss: 0.1979
11/48 āāāāāāāāāāāāāāāāāāāā 7s 192ms/step - accuracy: 0.8948 - loss: 0.1991
12/48 āāāāāāāāāāāāāāāāāāāā 6s 192ms/step - accuracy: 0.8953 - loss: 0.1998
13/48 āāāāāāāāāāāāāāāāāāāā 6s 193ms/step - accuracy: 0.8959 - loss: 0.2000
14/48 āāāāāāāāāāāāāāāāāāāā 6s 194ms/step - accuracy: 0.8968 - loss: 0.1998
15/48 āāāāāāāāāāāāāāāāāāāā 6s 194ms/step - accuracy: 0.8979 - loss: 0.1993
16/48 āāāāāāāāāāāāāāāāāāāā 6s 195ms/step - accuracy: 0.8991 - loss: 0.1986
17/48 āāāāāāāāāāāāāāāāāāāā 6s 195ms/step - accuracy: 0.9003 - loss: 0.1980
18/48 āāāāāāāāāāāāāāāāāāāā 5s 195ms/step - accuracy: 0.9014 - loss: 0.1974
19/48 āāāāāāāāāāāāāāāāāāāā 5s 195ms/step - accuracy: 0.9025 - loss: 0.1968
20/48 āāāāāāāāāāāāāāāāāāāā 5s 195ms/step - accuracy: 0.9036 - loss: 0.1962
21/48 āāāāāāāāāāāāāāāāāāāā 5s 195ms/step - accuracy: 0.9047 - loss: 0.1954
22/48 āāāāāāāāāāāāāāāāāāāā 5s 195ms/step - accuracy: 0.9058 - loss: 0.1945
23/48 āāāāāāāāāāāāāāāāāāāā 4s 195ms/step - accuracy: 0.9067 - loss: 0.1937
24/48 āāāāāāāāāāāāāāāāāāāā 4s 195ms/step - accuracy: 0.9076 - loss: 0.1929
25/48 āāāāāāāāāāāāāāāāāāāā 4s 194ms/step - accuracy: 0.9085 - loss: 0.1921
26/48 āāāāāāāāāāāāāāāāāāāā 4s 194ms/step - accuracy: 0.9091 - loss: 0.1915
27/48 āāāāāāāāāāāāāāāāāāāā 4s 194ms/step - accuracy: 0.9097 - loss: 0.1911
28/48 āāāāāāāāāāāāāāāāāāāā 3s 194ms/step - accuracy: 0.9102 - loss: 0.1908
29/48 āāāāāāāāāāāāāāāāāāāā 3s 194ms/step - accuracy: 0.9108 - loss: 0.1904
30/48 āāāāāāāāāāāāāāāāāāāā 3s 194ms/step - accuracy: 0.9113 - loss: 0.1900
31/48 āāāāāāāāāāāāāāāāāāāā 3s 194ms/step - accuracy: 0.9117 - loss: 0.1898
32/48 āāāāāāāāāāāāāāāāāāāā 3s 194ms/step - accuracy: 0.9122 - loss: 0.1896
33/48 āāāāāāāāāāāāāāāāāāāā 2s 194ms/step - accuracy: 0.9127 - loss: 0.1894
34/48 āāāāāāāāāāāāāāāāāāāā 2s 194ms/step - accuracy: 0.9131 - loss: 0.1891
35/48 āāāāāāāāāāāāāāāāāāāā 2s 194ms/step - accuracy: 0.9137 - loss: 0.1887
36/48 āāāāāāāāāāāāāāāāāāāā 2s 194ms/step - accuracy: 0.9142 - loss: 0.1884
37/48 āāāāāāāāāāāāāāāāāāāā 2s 194ms/step - accuracy: 0.9146 - loss: 0.1880
38/48 āāāāāāāāāāāāāāāāāāāā 1s 194ms/step - accuracy: 0.9151 - loss: 0.1876
39/48 āāāāāāāāāāāāāāāāāāāā 1s 194ms/step - accuracy: 0.9155 - loss: 0.1873
40/48 āāāāāāāāāāāāāāāāāāāā 1s 194ms/step - accuracy: 0.9159 - loss: 0.1871
41/48 āāāāāāāāāāāāāāāāāāāā 1s 194ms/step - accuracy: 0.9163 - loss: 0.1868
42/48 āāāāāāāāāāāāāāāāāāāā 1s 194ms/step - accuracy: 0.9167 - loss: 0.1865
43/48 āāāāāāāāāāāāāāāāāāāā 0s 194ms/step - accuracy: 0.9170 - loss: 0.1862
44/48 āāāāāāāāāāāāāāāāāāāā 0s 194ms/step - accuracy: 0.9174 - loss: 0.1860
45/48 āāāāāāāāāāāāāāāāāāāā 0s 194ms/step - accuracy: 0.9177 - loss: 0.1857
46/48 āāāāāāāāāāāāāāāāāāāā 0s 194ms/step - accuracy: 0.9180 - loss: 0.1855
47/48 āāāāāāāāāāāāāāāāāāāā 0s 194ms/step - accuracy: 0.9184 - loss: 0.1852
48/48 āāāāāāāāāāāāāāāāāāāā 0s 194ms/step - accuracy: 0.9187 - loss: 0.1849
48/48 āāāāāāāāāāāāāāāāāāāā 10s 209ms/step - accuracy: 0.9191 - loss: 0.1846 - val_accuracy: 0.7929 - val_loss: 0.4638
1/16 āāāāāāāāāāāāāāāāāāāā 1s 127ms/step
2/16 āāāāāāāāāāāāāāāāāāāā 0s 51ms/step
4/16 āāāāāāāāāāāāāāāāāāāā 0s 45ms/step
5/16 āāāāāāāāāāāāāāāāāāāā 0s 48ms/step
7/16 āāāāāāāāāāāāāāāāāāāā 0s 47ms/step
9/16 āāāāāāāāāāāāāāāāāāāā 0s 47ms/step
11/16 āāāāāāāāāāāāāāāāāāāā 0s 46ms/step
13/16 āāāāāāāāāāāāāāāāāāāā 0s 46ms/step
15/16 āāāāāāāāāāāāāāāāāāāā 0s 45ms/step
16/16 āāāāāāāāāāāāāāāāāāāā 0s 50ms/step
16/16 āāāāāāāāāāāāāāāāāāāā 1s 50ms/step
Accuracy: 0.7928994082840237 Precision: 0.755700325732899 Recall: 0.8854961832061069
Epoch 1/5
1/140 āāāāāāāāāāāāāāāāāāāā 2:29 1s/step - accuracy: 0.4375 - loss: 0.6937
2/140 āāāāāāāāāāāāāāāāāāāā 17s 126ms/step - accuracy: 0.4766 - loss: 0.6937
3/140 āāāāāāāāāāāāāāāāāāāā 17s 124ms/step - accuracy: 0.4948 - loss: 0.6937
4/140 āāāāāāāāāāāāāāāāāāāā 17s 126ms/step - accuracy: 0.4902 - loss: 0.6938
5/140 āāāāāāāāāāāāāāāāāāāā 16s 125ms/step - accuracy: 0.4884 - loss: 0.6938
6/140 āāāāāāāāāāāāāāāāāāāā 16s 125ms/step - accuracy: 0.4852 - loss: 0.6939
7/140 āāāāāāāāāāāāāāāāāāāā 16s 126ms/step - accuracy: 0.4828 - loss: 0.6939
8/140 āāāāāāāāāāāāāāāāāāāā 16s 126ms/step - accuracy: 0.4796 - loss: 0.6939
9/140 āāāāāāāāāāāāāāāāāāāā 16s 126ms/step - accuracy: 0.4784 - loss: 0.6939
10/140 āāāāāāāāāāāāāāāāāāāā 16s 126ms/step - accuracy: 0.4780 - loss: 0.6939
11/140 āāāāāāāāāāāāāāāāāāāā 16s 126ms/step - accuracy: 0.4782 - loss: 0.6938
12/140 āāāāāāāāāāāāāāāāāāāā 16s 125ms/step - accuracy: 0.4790 - loss: 0.6938
13/140 āāāāāāāāāāāāāāāāāāāā 15s 125ms/step - accuracy: 0.4791 - loss: 0.6938
14/140 āāāāāāāāāāāāāāāāāāāā 15s 125ms/step - accuracy: 0.4790 - loss: 0.6938
15/140 āāāāāāāāāāāāāāāāāāāā 15s 125ms/step - accuracy: 0.4787 - loss: 0.6938
16/140 āāāāāāāāāāāāāāāāāāāā 15s 125ms/step - accuracy: 0.4788 - loss: 0.6938
17/140 āāāāāāāāāāāāāāāāāāāā 15s 125ms/step - accuracy: 0.4795 - loss: 0.6938
18/140 āāāāāāāāāāāāāāāāāāāā 15s 125ms/step - accuracy: 0.4806 - loss: 0.6937
19/140 āāāāāāāāāāāāāāāāāāāā 15s 125ms/step - accuracy: 0.4818 - loss: 0.6937
20/140 āāāāāāāāāāāāāāāāāāāā 14s 125ms/step - accuracy: 0.4828 - loss: 0.6937
21/140 āāāāāāāāāāāāāāāāāāāā 14s 125ms/step - accuracy: 0.4841 - loss: 0.6937
22/140 āāāāāāāāāāāāāāāāāāāā 14s 125ms/step - accuracy: 0.4855 - loss: 0.6936
23/140 āāāāāāāāāāāāāāāāāāāā 14s 124ms/step - accuracy: 0.4869 - loss: 0.6936
24/140 āāāāāāāāāāāāāāāāāāāā 14s 125ms/step - accuracy: 0.4885 - loss: 0.6936
25/140 āāāāāāāāāāāāāāāāāāāā 14s 124ms/step - accuracy: 0.4901 - loss: 0.6935
26/140 āāāāāāāāāāāāāāāāāāāā 14s 125ms/step - accuracy: 0.4913 - loss: 0.6935
27/140 āāāāāāāāāāāāāāāāāāāā 14s 125ms/step - accuracy: 0.4924 - loss: 0.6935
28/140 āāāāāāāāāāāāāāāāāāāā 13s 125ms/step - accuracy: 0.4935 - loss: 0.6934
29/140 āāāāāāāāāāāāāāāāāāāā 13s 125ms/step - accuracy: 0.4945 - loss: 0.6934
30/140 āāāāāāāāāāāāāāāāāāāā 13s 125ms/step - accuracy: 0.4954 - loss: 0.6934
31/140 āāāāāāāāāāāāāāāāāāāā 13s 125ms/step - accuracy: 0.4963 - loss: 0.6933
32/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.4970 - loss: 0.6933
33/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.4976 - loss: 0.6933
34/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.4983 - loss: 0.6932
35/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.4988 - loss: 0.6932
36/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.4993 - loss: 0.6932
37/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.4996 - loss: 0.6932
38/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.5000 - loss: 0.6932
39/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.5002 - loss: 0.6931
40/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.5005 - loss: 0.6931
41/140 āāāāāāāāāāāāāāāāāāāā 12s 125ms/step - accuracy: 0.5007 - loss: 0.6931
42/140 āāāāāāāāāāāāāāāāāāāā 12s 125ms/step - accuracy: 0.5010 - loss: 0.6931
43/140 āāāāāāāāāāāāāāāāāāāā 12s 125ms/step - accuracy: 0.5012 - loss: 0.6930
44/140 āāāāāāāāāāāāāāāāāāāā 11s 125ms/step - accuracy: 0.5014 - loss: 0.6930
45/140 āāāāāāāāāāāāāāāāāāāā 11s 125ms/step - accuracy: 0.5017 - loss: 0.6930
46/140 āāāāāāāāāāāāāāāāāāāā 11s 125ms/step - accuracy: 0.5019 - loss: 0.6930
47/140 āāāāāāāāāāāāāāāāāāāā 11s 125ms/step - accuracy: 0.5022 - loss: 0.6929
48/140 āāāāāāāāāāāāāāāāāāāā 11s 125ms/step - accuracy: 0.5026 - loss: 0.6929
49/140 āāāāāāāāāāāāāāāāāāāā 11s 125ms/step - accuracy: 0.5029 - loss: 0.6929
50/140 āāāāāāāāāāāāāāāāāāāā 11s 125ms/step - accuracy: 0.5033 - loss: 0.6929
51/140 āāāāāāāāāāāāāāāāāāāā 11s 125ms/step - accuracy: 0.5037 - loss: 0.6928
52/140 āāāāāāāāāāāāāāāāāāāā 10s 125ms/step - accuracy: 0.5041 - loss: 0.6928
53/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.5046 - loss: 0.6928
54/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.5052 - loss: 0.6927
55/140 āāāāāāāāāāāāāāāāāāāā 10s 125ms/step - accuracy: 0.5058 - loss: 0.6927
56/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.5064 - loss: 0.6927
57/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.5070 - loss: 0.6926
58/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.5076 - loss: 0.6926
59/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.5083 - loss: 0.6925
60/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.5088 - loss: 0.6925
61/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.5094 - loss: 0.6924
62/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.5100 - loss: 0.6924
63/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.5106 - loss: 0.6923
64/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.5113 - loss: 0.6923
65/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.5119 - loss: 0.6922
66/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.5125 - loss: 0.6922
67/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.5132 - loss: 0.6921
68/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.5138 - loss: 0.6921
69/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.5144 - loss: 0.6920
70/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.5150 - loss: 0.6919
71/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.5156 - loss: 0.6919
72/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.5162 - loss: 0.6918
73/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.5169 - loss: 0.6917
74/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.5175 - loss: 0.6916
75/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.5181 - loss: 0.6915
76/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.5188 - loss: 0.6914
77/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.5194 - loss: 0.6913
78/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.5201 - loss: 0.6912
79/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.5208 - loss: 0.6911
80/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.5214 - loss: 0.6910
81/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.5221 - loss: 0.6909
82/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.5227 - loss: 0.6908
83/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.5234 - loss: 0.6906
84/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.5241 - loss: 0.6905
85/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.5248 - loss: 0.6904
86/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.5255 - loss: 0.6902
87/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.5262 - loss: 0.6901
88/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.5269 - loss: 0.6899
89/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.5276 - loss: 0.6897
90/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.5284 - loss: 0.6896
91/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.5291 - loss: 0.6894
92/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.5298 - loss: 0.6892
93/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.5305 - loss: 0.6890
94/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.5312 - loss: 0.6889
95/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.5319 - loss: 0.6887
96/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.5326 - loss: 0.6885
97/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.5333 - loss: 0.6883
98/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.5339 - loss: 0.6881
99/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.5346 - loss: 0.6879
100/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.5353 - loss: 0.6877
101/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.5359 - loss: 0.6875
102/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.5366 - loss: 0.6872
103/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.5372 - loss: 0.6870
104/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.5379 - loss: 0.6868
105/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.5385 - loss: 0.6866
106/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.5391 - loss: 0.6863
107/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.5397 - loss: 0.6861
108/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.5403 - loss: 0.6859
109/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.5410 - loss: 0.6856
110/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.5416 - loss: 0.6854
111/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.5422 - loss: 0.6851
112/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.5428 - loss: 0.6849
113/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.5435 - loss: 0.6846
114/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.5441 - loss: 0.6843
115/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.5447 - loss: 0.6840
116/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.5454 - loss: 0.6837
117/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.5460 - loss: 0.6834
118/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.5466 - loss: 0.6831
119/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.5473 - loss: 0.6828
120/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.5479 - loss: 0.6825
121/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.5485 - loss: 0.6822
122/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.5492 - loss: 0.6819
123/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.5498 - loss: 0.6816
124/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.5504 - loss: 0.6812
125/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.5511 - loss: 0.6809
126/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.5517 - loss: 0.6805
127/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.5523 - loss: 0.6802
128/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.5530 - loss: 0.6799
129/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.5536 - loss: 0.6795
130/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.5542 - loss: 0.6792
131/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.5548 - loss: 0.6788
132/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.5554 - loss: 0.6785
133/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.5561 - loss: 0.6781
134/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.5567 - loss: 0.6777
135/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.5573 - loss: 0.6774
136/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.5579 - loss: 0.6770
137/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.5586 - loss: 0.6766
138/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.5592 - loss: 0.6762
139/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.5598 - loss: 0.6759
140/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.5604 - loss: 0.6755
140/140 āāāāāāāāāāāāāāāāāāāā 20s 136ms/step - accuracy: 0.5610 - loss: 0.6751 - val_accuracy: 0.7776 - val_loss: 0.4669
Epoch 2/5
1/140 āāāāāāāāāāāāāāāāāāāā 18s 136ms/step - accuracy: 0.8750 - loss: 0.3674
2/140 āāāāāāāāāāāāāāāāāāāā 17s 129ms/step - accuracy: 0.8906 - loss: 0.3632
3/140 āāāāāāāāāāāāāāāāāāāā 17s 131ms/step - accuracy: 0.8750 - loss: 0.3739
4/140 āāāāāāāāāāāāāāāāāāāā 17s 128ms/step - accuracy: 0.8672 - loss: 0.3805
5/140 āāāāāāāāāāāāāāāāāāāā 17s 126ms/step - accuracy: 0.8662 - loss: 0.3818
6/140 āāāāāāāāāāāāāāāāāāāā 17s 127ms/step - accuracy: 0.8660 - loss: 0.3812
7/140 āāāāāāāāāāāāāāāāāāāā 16s 127ms/step - accuracy: 0.8666 - loss: 0.3806
8/140 āāāāāāāāāāāāāāāāāāāā 16s 128ms/step - accuracy: 0.8667 - loss: 0.3801
9/140 āāāāāāāāāāāāāāāāāāāā 16s 129ms/step - accuracy: 0.8668 - loss: 0.3796
10/140 āāāāāāāāāāāāāāāāāāāā 16s 129ms/step - accuracy: 0.8661 - loss: 0.3797
11/140 āāāāāāāāāāāāāāāāāāāā 16s 128ms/step - accuracy: 0.8651 - loss: 0.3798
12/140 āāāāāāāāāāāāāāāāāāāā 16s 128ms/step - accuracy: 0.8638 - loss: 0.3804
13/140 āāāāāāāāāāāāāāāāāāāā 16s 128ms/step - accuracy: 0.8622 - loss: 0.3808
14/140 āāāāāāāāāāāāāāāāāāāā 16s 128ms/step - accuracy: 0.8604 - loss: 0.3813
15/140 āāāāāāāāāāāāāāāāāāāā 15s 128ms/step - accuracy: 0.8588 - loss: 0.3815
16/140 āāāāāāāāāāāāāāāāāāāā 15s 128ms/step - accuracy: 0.8572 - loss: 0.3819
17/140 āāāāāāāāāāāāāāāāāāāā 15s 128ms/step - accuracy: 0.8559 - loss: 0.3822
18/140 āāāāāāāāāāāāāāāāāāāā 15s 128ms/step - accuracy: 0.8543 - loss: 0.3828
19/140 āāāāāāāāāāāāāāāāāāāā 15s 128ms/step - accuracy: 0.8532 - loss: 0.3833
20/140 āāāāāāāāāāāāāāāāāāāā 15s 128ms/step - accuracy: 0.8519 - loss: 0.3836
21/140 āāāāāāāāāāāāāāāāāāāā 15s 128ms/step - accuracy: 0.8507 - loss: 0.3840
22/140 āāāāāāāāāāāāāāāāāāāā 15s 128ms/step - accuracy: 0.8497 - loss: 0.3844
23/140 āāāāāāāāāāāāāāāāāāāā 14s 127ms/step - accuracy: 0.8486 - loss: 0.3846
24/140 āāāāāāāāāāāāāāāāāāāā 14s 127ms/step - accuracy: 0.8477 - loss: 0.3848
25/140 āāāāāāāāāāāāāāāāāāāā 14s 127ms/step - accuracy: 0.8467 - loss: 0.3850
26/140 āāāāāāāāāāāāāāāāāāāā 14s 127ms/step - accuracy: 0.8458 - loss: 0.3851
27/140 āāāāāāāāāāāāāāāāāāāā 14s 126ms/step - accuracy: 0.8450 - loss: 0.3853
28/140 āāāāāāāāāāāāāāāāāāāā 14s 126ms/step - accuracy: 0.8442 - loss: 0.3855
29/140 āāāāāāāāāāāāāāāāāāāā 14s 126ms/step - accuracy: 0.8434 - loss: 0.3856
30/140 āāāāāāāāāāāāāāāāāāāā 13s 126ms/step - accuracy: 0.8427 - loss: 0.3857
31/140 āāāāāāāāāāāāāāāāāāāā 13s 126ms/step - accuracy: 0.8421 - loss: 0.3857
32/140 āāāāāāāāāāāāāāāāāāāā 13s 126ms/step - accuracy: 0.8417 - loss: 0.3855
33/140 āāāāāāāāāāāāāāāāāāāā 13s 126ms/step - accuracy: 0.8412 - loss: 0.3854
34/140 āāāāāāāāāāāāāāāāāāāā 13s 126ms/step - accuracy: 0.8407 - loss: 0.3853
35/140 āāāāāāāāāāāāāāāāāāāā 13s 126ms/step - accuracy: 0.8404 - loss: 0.3851
36/140 āāāāāāāāāāāāāāāāāāāā 13s 126ms/step - accuracy: 0.8401 - loss: 0.3849
37/140 āāāāāāāāāāāāāāāāāāāā 12s 126ms/step - accuracy: 0.8398 - loss: 0.3847
38/140 āāāāāāāāāāāāāāāāāāāā 12s 126ms/step - accuracy: 0.8396 - loss: 0.3844
39/140 āāāāāāāāāāāāāāāāāāāā 12s 126ms/step - accuracy: 0.8394 - loss: 0.3841
40/140 āāāāāāāāāāāāāāāāāāāā 12s 126ms/step - accuracy: 0.8393 - loss: 0.3838
41/140 āāāāāāāāāāāāāāāāāāāā 12s 126ms/step - accuracy: 0.8391 - loss: 0.3835
42/140 āāāāāāāāāāāāāāāāāāāā 12s 126ms/step - accuracy: 0.8390 - loss: 0.3833
43/140 āāāāāāāāāāāāāāāāāāāā 12s 126ms/step - accuracy: 0.8389 - loss: 0.3830
44/140 āāāāāāāāāāāāāāāāāāāā 12s 126ms/step - accuracy: 0.8389 - loss: 0.3827
45/140 āāāāāāāāāāāāāāāāāāāā 11s 126ms/step - accuracy: 0.8388 - loss: 0.3824
46/140 āāāāāāāāāāāāāāāāāāāā 11s 126ms/step - accuracy: 0.8388 - loss: 0.3820
47/140 āāāāāāāāāāāāāāāāāāāā 11s 126ms/step - accuracy: 0.8388 - loss: 0.3816
48/140 āāāāāāāāāāāāāāāāāāāā 11s 126ms/step - accuracy: 0.8388 - loss: 0.3812
49/140 āāāāāāāāāāāāāāāāāāāā 11s 126ms/step - accuracy: 0.8388 - loss: 0.3809
50/140 āāāāāāāāāāāāāāāāāāāā 11s 126ms/step - accuracy: 0.8388 - loss: 0.3806
51/140 āāāāāāāāāāāāāāāāāāāā 11s 126ms/step - accuracy: 0.8387 - loss: 0.3803
52/140 āāāāāāāāāāāāāāāāāāāā 11s 126ms/step - accuracy: 0.8387 - loss: 0.3800
53/140 āāāāāāāāāāāāāāāāāāāā 10s 126ms/step - accuracy: 0.8387 - loss: 0.3797
54/140 āāāāāāāāāāāāāāāāāāāā 10s 126ms/step - accuracy: 0.8386 - loss: 0.3795
55/140 āāāāāāāāāāāāāāāāāāāā 10s 126ms/step - accuracy: 0.8386 - loss: 0.3793
56/140 āāāāāāāāāāāāāāāāāāāā 10s 126ms/step - accuracy: 0.8385 - loss: 0.3790
57/140 āāāāāāāāāāāāāāāāāāāā 10s 126ms/step - accuracy: 0.8385 - loss: 0.3787
58/140 āāāāāāāāāāāāāāāāāāāā 10s 126ms/step - accuracy: 0.8385 - loss: 0.3785
59/140 āāāāāāāāāāāāāāāāāāāā 10s 126ms/step - accuracy: 0.8385 - loss: 0.3782
60/140 āāāāāāāāāāāāāāāāāāāā 10s 125ms/step - accuracy: 0.8385 - loss: 0.3779
61/140 āāāāāāāāāāāāāāāāāāāā 9s 125ms/step - accuracy: 0.8386 - loss: 0.3776
62/140 āāāāāāāāāāāāāāāāāāāā 9s 125ms/step - accuracy: 0.8386 - loss: 0.3772
63/140 āāāāāāāāāāāāāāāāāāāā 9s 126ms/step - accuracy: 0.8386 - loss: 0.3769
64/140 āāāāāāāāāāāāāāāāāāāā 9s 125ms/step - accuracy: 0.8387 - loss: 0.3766
65/140 āāāāāāāāāāāāāāāāāāāā 9s 125ms/step - accuracy: 0.8388 - loss: 0.3763
66/140 āāāāāāāāāāāāāāāāāāāā 9s 125ms/step - accuracy: 0.8388 - loss: 0.3759
67/140 āāāāāāāāāāāāāāāāāāāā 9s 125ms/step - accuracy: 0.8389 - loss: 0.3756
68/140 āāāāāāāāāāāāāāāāāāāā 9s 125ms/step - accuracy: 0.8389 - loss: 0.3753
69/140 āāāāāāāāāāāāāāāāāāāā 8s 125ms/step - accuracy: 0.8390 - loss: 0.3750
70/140 āāāāāāāāāāāāāāāāāāāā 8s 125ms/step - accuracy: 0.8391 - loss: 0.3746
71/140 āāāāāāāāāāāāāāāāāāāā 8s 125ms/step - accuracy: 0.8391 - loss: 0.3743
72/140 āāāāāāāāāāāāāāāāāāāā 8s 125ms/step - accuracy: 0.8392 - loss: 0.3740
73/140 āāāāāāāāāāāāāāāāāāāā 8s 125ms/step - accuracy: 0.8393 - loss: 0.3737
74/140 āāāāāāāāāāāāāāāāāāāā 8s 125ms/step - accuracy: 0.8393 - loss: 0.3734
75/140 āāāāāāāāāāāāāāāāāāāā 8s 125ms/step - accuracy: 0.8394 - loss: 0.3731
76/140 āāāāāāāāāāāāāāāāāāāā 8s 125ms/step - accuracy: 0.8395 - loss: 0.3728
77/140 āāāāāāāāāāāāāāāāāāāā 7s 125ms/step - accuracy: 0.8396 - loss: 0.3725
78/140 āāāāāāāāāāāāāāāāāāāā 7s 125ms/step - accuracy: 0.8396 - loss: 0.3722
79/140 āāāāāāāāāāāāāāāāāāāā 7s 125ms/step - accuracy: 0.8397 - loss: 0.3720
80/140 āāāāāāāāāāāāāāāāāāāā 7s 125ms/step - accuracy: 0.8398 - loss: 0.3717
81/140 āāāāāāāāāāāāāāāāāāāā 7s 125ms/step - accuracy: 0.8399 - loss: 0.3714
82/140 āāāāāāāāāāāāāāāāāāāā 7s 125ms/step - accuracy: 0.8400 - loss: 0.3711
83/140 āāāāāāāāāāāāāāāāāāāā 7s 125ms/step - accuracy: 0.8401 - loss: 0.3708
84/140 āāāāāāāāāāāāāāāāāāāā 6s 125ms/step - accuracy: 0.8402 - loss: 0.3705
85/140 āāāāāāāāāāāāāāāāāāāā 6s 125ms/step - accuracy: 0.8403 - loss: 0.3702
86/140 āāāāāāāāāāāāāāāāāāāā 6s 125ms/step - accuracy: 0.8404 - loss: 0.3699
87/140 āāāāāāāāāāāāāāāāāāāā 6s 125ms/step - accuracy: 0.8404 - loss: 0.3697
88/140 āāāāāāāāāāāāāāāāāāāā 6s 125ms/step - accuracy: 0.8405 - loss: 0.3694
89/140 āāāāāāāāāāāāāāāāāāāā 6s 125ms/step - accuracy: 0.8406 - loss: 0.3692
90/140 āāāāāāāāāāāāāāāāāāāā 6s 125ms/step - accuracy: 0.8406 - loss: 0.3689
91/140 āāāāāāāāāāāāāāāāāāāā 6s 125ms/step - accuracy: 0.8407 - loss: 0.3687
92/140 āāāāāāāāāāāāāāāāāāāā 5s 125ms/step - accuracy: 0.8408 - loss: 0.3684
93/140 āāāāāāāāāāāāāāāāāāāā 5s 125ms/step - accuracy: 0.8408 - loss: 0.3682
94/140 āāāāāāāāāāāāāāāāāāāā 5s 125ms/step - accuracy: 0.8409 - loss: 0.3680
95/140 āāāāāāāāāāāāāāāāāāāā 5s 125ms/step - accuracy: 0.8409 - loss: 0.3677
96/140 āāāāāāāāāāāāāāāāāāāā 5s 125ms/step - accuracy: 0.8410 - loss: 0.3675
97/140 āāāāāāāāāāāāāāāāāāāā 5s 125ms/step - accuracy: 0.8411 - loss: 0.3673
98/140 āāāāāāāāāāāāāāāāāāāā 5s 125ms/step - accuracy: 0.8411 - loss: 0.3670
99/140 āāāāāāāāāāāāāāāāāāāā 5s 125ms/step - accuracy: 0.8412 - loss: 0.3668
100/140 āāāāāāāāāāāāāāāāāāāā 4s 125ms/step - accuracy: 0.8412 - loss: 0.3666
101/140 āāāāāāāāāāāāāāāāāāāā 4s 125ms/step - accuracy: 0.8413 - loss: 0.3664
102/140 āāāāāāāāāāāāāāāāāāāā 4s 125ms/step - accuracy: 0.8413 - loss: 0.3661
103/140 āāāāāāāāāāāāāāāāāāāā 4s 125ms/step - accuracy: 0.8414 - loss: 0.3659
104/140 āāāāāāāāāāāāāāāāāāāā 4s 125ms/step - accuracy: 0.8415 - loss: 0.3657
105/140 āāāāāāāāāāāāāāāāāāāā 4s 125ms/step - accuracy: 0.8415 - loss: 0.3655
106/140 āāāāāāāāāāāāāāāāāāāā 4s 125ms/step - accuracy: 0.8416 - loss: 0.3653
107/140 āāāāāāāāāāāāāāāāāāāā 4s 125ms/step - accuracy: 0.8417 - loss: 0.3651
108/140 āāāāāāāāāāāāāāāāāāāā 3s 125ms/step - accuracy: 0.8417 - loss: 0.3649
109/140 āāāāāāāāāāāāāāāāāāāā 3s 125ms/step - accuracy: 0.8418 - loss: 0.3647
110/140 āāāāāāāāāāāāāāāāāāāā 3s 125ms/step - accuracy: 0.8419 - loss: 0.3645
111/140 āāāāāāāāāāāāāāāāāāāā 3s 125ms/step - accuracy: 0.8419 - loss: 0.3643
112/140 āāāāāāāāāāāāāāāāāāāā 3s 125ms/step - accuracy: 0.8420 - loss: 0.3641
113/140 āāāāāāāāāāāāāāāāāāāā 3s 125ms/step - accuracy: 0.8421 - loss: 0.3639
114/140 āāāāāāāāāāāāāāāāāāāā 3s 125ms/step - accuracy: 0.8422 - loss: 0.3637
115/140 āāāāāāāāāāāāāāāāāāāā 3s 125ms/step - accuracy: 0.8422 - loss: 0.3634
116/140 āāāāāāāāāāāāāāāāāāāā 2s 125ms/step - accuracy: 0.8423 - loss: 0.3632
117/140 āāāāāāāāāāāāāāāāāāāā 2s 125ms/step - accuracy: 0.8424 - loss: 0.3630
118/140 āāāāāāāāāāāāāāāāāāāā 2s 125ms/step - accuracy: 0.8425 - loss: 0.3628
119/140 āāāāāāāāāāāāāāāāāāāā 2s 125ms/step - accuracy: 0.8425 - loss: 0.3626
120/140 āāāāāāāāāāāāāāāāāāāā 2s 125ms/step - accuracy: 0.8426 - loss: 0.3624
121/140 āāāāāāāāāāāāāāāāāāāā 2s 125ms/step - accuracy: 0.8427 - loss: 0.3622
122/140 āāāāāāāāāāāāāāāāāāāā 2s 125ms/step - accuracy: 0.8428 - loss: 0.3620
123/140 āāāāāāāāāāāāāāāāāāāā 2s 125ms/step - accuracy: 0.8428 - loss: 0.3618
124/140 āāāāāāāāāāāāāāāāāāāā 1s 125ms/step - accuracy: 0.8429 - loss: 0.3616
125/140 āāāāāāāāāāāāāāāāāāāā 1s 125ms/step - accuracy: 0.8430 - loss: 0.3614
126/140 āāāāāāāāāāāāāāāāāāāā 1s 125ms/step - accuracy: 0.8431 - loss: 0.3612
127/140 āāāāāāāāāāāāāāāāāāāā 1s 125ms/step - accuracy: 0.8431 - loss: 0.3610
128/140 āāāāāāāāāāāāāāāāāāāā 1s 125ms/step - accuracy: 0.8432 - loss: 0.3607
129/140 āāāāāāāāāāāāāāāāāāāā 1s 125ms/step - accuracy: 0.8433 - loss: 0.3605
130/140 āāāāāāāāāāāāāāāāāāāā 1s 125ms/step - accuracy: 0.8434 - loss: 0.3603
131/140 āāāāāāāāāāāāāāāāāāāā 1s 125ms/step - accuracy: 0.8435 - loss: 0.3601
132/140 āāāāāāāāāāāāāāāāāāāā 0s 125ms/step - accuracy: 0.8435 - loss: 0.3599
133/140 āāāāāāāāāāāāāāāāāāāā 0s 125ms/step - accuracy: 0.8436 - loss: 0.3597
134/140 āāāāāāāāāāāāāāāāāāāā 0s 125ms/step - accuracy: 0.8437 - loss: 0.3595
135/140 āāāāāāāāāāāāāāāāāāāā 0s 125ms/step - accuracy: 0.8438 - loss: 0.3593
136/140 āāāāāāāāāāāāāāāāāāāā 0s 125ms/step - accuracy: 0.8438 - loss: 0.3592
137/140 āāāāāāāāāāāāāāāāāāāā 0s 125ms/step - accuracy: 0.8439 - loss: 0.3590
138/140 āāāāāāāāāāāāāāāāāāāā 0s 125ms/step - accuracy: 0.8440 - loss: 0.3588
139/140 āāāāāāāāāāāāāāāāāāāā 0s 125ms/step - accuracy: 0.8440 - loss: 0.3586
140/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.8441 - loss: 0.3584
140/140 āāāāāāāāāāāāāāāāāāāā 19s 136ms/step - accuracy: 0.8442 - loss: 0.3582 - val_accuracy: 0.8376 - val_loss: 0.3601
Epoch 3/5
1/140 āāāāāāāāāāāāāāāāāāāā 17s 129ms/step - accuracy: 0.8125 - loss: 0.3579
2/140 āāāāāāāāāāāāāāāāāāāā 18s 132ms/step - accuracy: 0.8594 - loss: 0.2930
3/140 āāāāāāāāāāāāāāāāāāāā 17s 127ms/step - accuracy: 0.8611 - loss: 0.2813
4/140 āāāāāāāāāāāāāāāāāāāā 17s 125ms/step - accuracy: 0.8665 - loss: 0.2700
5/140 āāāāāāāāāāāāāāāāāāāā 16s 125ms/step - accuracy: 0.8695 - loss: 0.2689
6/140 āāāāāāāāāāāāāāāāāāāā 16s 124ms/step - accuracy: 0.8730 - loss: 0.2678
7/140 āāāāāāāāāāāāāāāāāāāā 16s 123ms/step - accuracy: 0.8771 - loss: 0.2661
8/140 āāāāāāāāāāāāāāāāāāāā 16s 124ms/step - accuracy: 0.8803 - loss: 0.2638
9/140 āāāāāāāāāāāāāāāāāāāā 16s 124ms/step - accuracy: 0.8828 - loss: 0.2620
10/140 āāāāāāāāāāāāāāāāāāāā 16s 124ms/step - accuracy: 0.8845 - loss: 0.2599
11/140 āāāāāāāāāāāāāāāāāāāā 16s 124ms/step - accuracy: 0.8860 - loss: 0.2578
12/140 āāāāāāāāāāāāāāāāāāāā 15s 124ms/step - accuracy: 0.8879 - loss: 0.2555
13/140 āāāāāāāāāāāāāāāāāāāā 15s 124ms/step - accuracy: 0.8893 - loss: 0.2537
14/140 āāāāāāāāāāāāāāāāāāāā 15s 124ms/step - accuracy: 0.8903 - loss: 0.2523
15/140 āāāāāāāāāāāāāāāāāāāā 15s 124ms/step - accuracy: 0.8917 - loss: 0.2505
16/140 āāāāāāāāāāāāāāāāāāāā 15s 124ms/step - accuracy: 0.8931 - loss: 0.2486
17/140 āāāāāāāāāāāāāāāāāāāā 15s 124ms/step - accuracy: 0.8943 - loss: 0.2469
18/140 āāāāāāāāāāāāāāāāāāāā 15s 124ms/step - accuracy: 0.8953 - loss: 0.2454
19/140 āāāāāāāāāāāāāāāāāāāā 15s 124ms/step - accuracy: 0.8962 - loss: 0.2440
20/140 āāāāāāāāāāāāāāāāāāāā 14s 124ms/step - accuracy: 0.8971 - loss: 0.2427
21/140 āāāāāāāāāāāāāāāāāāāā 14s 124ms/step - accuracy: 0.8979 - loss: 0.2413
22/140 āāāāāāāāāāāāāāāāāāāā 14s 124ms/step - accuracy: 0.8986 - loss: 0.2399
23/140 āāāāāāāāāāāāāāāāāāāā 14s 124ms/step - accuracy: 0.8994 - loss: 0.2385
24/140 āāāāāāāāāāāāāāāāāāāā 14s 125ms/step - accuracy: 0.9000 - loss: 0.2372
25/140 āāāāāāāāāāāāāāāāāāāā 14s 124ms/step - accuracy: 0.9006 - loss: 0.2359
26/140 āāāāāāāāāāāāāāāāāāāā 14s 124ms/step - accuracy: 0.9011 - loss: 0.2348
27/140 āāāāāāāāāāāāāāāāāāāā 14s 124ms/step - accuracy: 0.9016 - loss: 0.2338
28/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9021 - loss: 0.2329
29/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9026 - loss: 0.2320
30/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9031 - loss: 0.2311
31/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9036 - loss: 0.2303
32/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9041 - loss: 0.2295
33/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9045 - loss: 0.2288
34/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9048 - loss: 0.2282
35/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9052 - loss: 0.2276
36/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.9055 - loss: 0.2269
37/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.9059 - loss: 0.2262
38/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.9062 - loss: 0.2256
39/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.9065 - loss: 0.2250
40/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.9068 - loss: 0.2244
41/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.9071 - loss: 0.2238
42/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.9074 - loss: 0.2232
43/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9077 - loss: 0.2227
44/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9080 - loss: 0.2221
45/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9082 - loss: 0.2217
46/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9085 - loss: 0.2212
47/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9087 - loss: 0.2207
48/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9089 - loss: 0.2203
49/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9091 - loss: 0.2200
50/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9094 - loss: 0.2196
51/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9096 - loss: 0.2193
52/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9099 - loss: 0.2189
53/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9101 - loss: 0.2186
54/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9103 - loss: 0.2183
55/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9106 - loss: 0.2180
56/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9108 - loss: 0.2177
57/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9110 - loss: 0.2174
58/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9113 - loss: 0.2171
59/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9115 - loss: 0.2167
60/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9117 - loss: 0.2164
61/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9119 - loss: 0.2161
62/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9121 - loss: 0.2159
63/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9123 - loss: 0.2156
64/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9124 - loss: 0.2153
65/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9126 - loss: 0.2150
66/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9128 - loss: 0.2147
67/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9130 - loss: 0.2144
68/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9131 - loss: 0.2141
69/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9133 - loss: 0.2139
70/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9135 - loss: 0.2136
71/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9136 - loss: 0.2133
72/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9138 - loss: 0.2130
73/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9139 - loss: 0.2127
74/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9141 - loss: 0.2125
75/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9142 - loss: 0.2122
76/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9143 - loss: 0.2119
77/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9144 - loss: 0.2117
78/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9146 - loss: 0.2114
79/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9147 - loss: 0.2112
80/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9148 - loss: 0.2109
81/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9149 - loss: 0.2106
82/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9151 - loss: 0.2104
83/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9152 - loss: 0.2101
84/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9153 - loss: 0.2099
85/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9154 - loss: 0.2097
86/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9155 - loss: 0.2095
87/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9156 - loss: 0.2092
88/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9157 - loss: 0.2090
89/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9158 - loss: 0.2088
90/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9159 - loss: 0.2086
91/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9160 - loss: 0.2084
92/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9161 - loss: 0.2081
93/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9162 - loss: 0.2079
94/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9163 - loss: 0.2077
95/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9164 - loss: 0.2075
96/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9165 - loss: 0.2073
97/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9166 - loss: 0.2071
98/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9167 - loss: 0.2069
99/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9168 - loss: 0.2066
100/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9169 - loss: 0.2064
101/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9169 - loss: 0.2062
102/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9170 - loss: 0.2061
103/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9171 - loss: 0.2059
104/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9172 - loss: 0.2057
105/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9173 - loss: 0.2055
106/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9173 - loss: 0.2053
107/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9174 - loss: 0.2051
108/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9175 - loss: 0.2050
109/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9175 - loss: 0.2048
110/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9176 - loss: 0.2047
111/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9176 - loss: 0.2045
112/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9177 - loss: 0.2044
113/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9177 - loss: 0.2042
114/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9178 - loss: 0.2041
115/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9178 - loss: 0.2040
116/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9179 - loss: 0.2038
117/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9179 - loss: 0.2037
118/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9179 - loss: 0.2036
119/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9180 - loss: 0.2035
120/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9180 - loss: 0.2034
121/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9180 - loss: 0.2033
122/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9180 - loss: 0.2032
123/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9181 - loss: 0.2031
124/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.9181 - loss: 0.2029
125/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.9181 - loss: 0.2028
126/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.9182 - loss: 0.2027
127/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.9182 - loss: 0.2026
128/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.9182 - loss: 0.2025
129/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.9182 - loss: 0.2024
130/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.9182 - loss: 0.2023
131/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.9183 - loss: 0.2022
132/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.9183 - loss: 0.2021
133/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.9183 - loss: 0.2020
134/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.9183 - loss: 0.2019
135/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.9183 - loss: 0.2018
136/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.9184 - loss: 0.2017
137/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.9184 - loss: 0.2016
138/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.9184 - loss: 0.2015
139/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.9184 - loss: 0.2014
140/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.9185 - loss: 0.2013
140/140 āāāāāāāāāāāāāāāāāāāā 19s 135ms/step - accuracy: 0.9185 - loss: 0.2013 - val_accuracy: 0.8457 - val_loss: 0.3693
Epoch 4/5
1/140 āāāāāāāāāāāāāāāāāāāā 19s 139ms/step - accuracy: 0.9375 - loss: 0.1481
2/140 āāāāāāāāāāāāāāāāāāāā 17s 124ms/step - accuracy: 0.9453 - loss: 0.1378
3/140 āāāāāāāāāāāāāāāāāāāā 16s 123ms/step - accuracy: 0.9427 - loss: 0.1379
4/140 āāāāāāāāāāāāāāāāāāāā 16s 122ms/step - accuracy: 0.9434 - loss: 0.1336
5/140 āāāāāāāāāāāāāāāāāāāā 16s 123ms/step - accuracy: 0.9447 - loss: 0.1310
6/140 āāāāāāāāāāāāāāāāāāāā 16s 123ms/step - accuracy: 0.9470 - loss: 0.1290
7/140 āāāāāāāāāāāāāāāāāāāā 16s 122ms/step - accuracy: 0.9482 - loss: 0.1280
8/140 āāāāāāāāāāāāāāāāāāāā 16s 123ms/step - accuracy: 0.9493 - loss: 0.1276
9/140 āāāāāāāāāāāāāāāāāāāā 16s 123ms/step - accuracy: 0.9507 - loss: 0.1265
10/140 āāāāāāāāāāāāāāāāāāāā 15s 123ms/step - accuracy: 0.9518 - loss: 0.1253
11/140 āāāāāāāāāāāāāāāāāāāā 15s 123ms/step - accuracy: 0.9526 - loss: 0.1247
12/140 āāāāāāāāāāāāāāāāāāāā 15s 123ms/step - accuracy: 0.9535 - loss: 0.1238
13/140 āāāāāāāāāāāāāāāāāāāā 15s 123ms/step - accuracy: 0.9541 - loss: 0.1231
14/140 āāāāāāāāāāāāāāāāāāāā 15s 123ms/step - accuracy: 0.9545 - loss: 0.1226
15/140 āāāāāāāāāāāāāāāāāāāā 15s 123ms/step - accuracy: 0.9547 - loss: 0.1222
16/140 āāāāāāāāāāāāāāāāāāāā 15s 124ms/step - accuracy: 0.9548 - loss: 0.1219
17/140 āāāāāāāāāāāāāāāāāāāā 15s 124ms/step - accuracy: 0.9550 - loss: 0.1216
18/140 āāāāāāāāāāāāāāāāāāāā 15s 124ms/step - accuracy: 0.9551 - loss: 0.1214
19/140 āāāāāāāāāāāāāāāāāāāā 14s 123ms/step - accuracy: 0.9553 - loss: 0.1211
20/140 āāāāāāāāāāāāāāāāāāāā 14s 123ms/step - accuracy: 0.9555 - loss: 0.1208
21/140 āāāāāāāāāāāāāāāāāāāā 14s 123ms/step - accuracy: 0.9558 - loss: 0.1206
22/140 āāāāāāāāāāāāāāāāāāāā 14s 123ms/step - accuracy: 0.9560 - loss: 0.1204
23/140 āāāāāāāāāāāāāāāāāāāā 14s 123ms/step - accuracy: 0.9562 - loss: 0.1202
24/140 āāāāāāāāāāāāāāāāāāāā 14s 124ms/step - accuracy: 0.9564 - loss: 0.1201
25/140 āāāāāāāāāāāāāāāāāāāā 14s 124ms/step - accuracy: 0.9565 - loss: 0.1201
26/140 āāāāāāāāāāāāāāāāāāāā 14s 124ms/step - accuracy: 0.9566 - loss: 0.1201
27/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9567 - loss: 0.1200
28/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9568 - loss: 0.1200
29/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9569 - loss: 0.1200
30/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9570 - loss: 0.1199
31/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9571 - loss: 0.1198
32/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9572 - loss: 0.1198
33/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9573 - loss: 0.1197
34/140 āāāāāāāāāāāāāāāāāāāā 13s 123ms/step - accuracy: 0.9573 - loss: 0.1197
35/140 āāāāāāāāāāāāāāāāāāāā 12s 123ms/step - accuracy: 0.9574 - loss: 0.1196
36/140 āāāāāāāāāāāāāāāāāāāā 12s 123ms/step - accuracy: 0.9574 - loss: 0.1195
37/140 āāāāāāāāāāāāāāāāāāāā 12s 123ms/step - accuracy: 0.9575 - loss: 0.1193
38/140 āāāāāāāāāāāāāāāāāāāā 12s 123ms/step - accuracy: 0.9576 - loss: 0.1192
39/140 āāāāāāāāāāāāāāāāāāāā 12s 123ms/step - accuracy: 0.9577 - loss: 0.1190
40/140 āāāāāāāāāāāāāāāāāāāā 12s 123ms/step - accuracy: 0.9579 - loss: 0.1188
41/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.9580 - loss: 0.1185
42/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.9581 - loss: 0.1183
43/140 āāāāāāāāāāāāāāāāāāāā 11s 123ms/step - accuracy: 0.9582 - loss: 0.1181
44/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9584 - loss: 0.1178
45/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9585 - loss: 0.1176
46/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9586 - loss: 0.1174
47/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9587 - loss: 0.1173
48/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9587 - loss: 0.1171
49/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9588 - loss: 0.1170
50/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9588 - loss: 0.1170
51/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9589 - loss: 0.1169
52/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9589 - loss: 0.1169
53/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9589 - loss: 0.1168
54/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9590 - loss: 0.1168
55/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9590 - loss: 0.1168
56/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9590 - loss: 0.1168
57/140 āāāāāāāāāāāāāāāāāāāā 10s 123ms/step - accuracy: 0.9591 - loss: 0.1167
58/140 āāāāāāāāāāāāāāāāāāāā 10s 123ms/step - accuracy: 0.9591 - loss: 0.1167
59/140 āāāāāāāāāāāāāāāāāāāā 9s 123ms/step - accuracy: 0.9592 - loss: 0.1167
60/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9593 - loss: 0.1166
61/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9593 - loss: 0.1166
62/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9594 - loss: 0.1165
63/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9594 - loss: 0.1165
64/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9595 - loss: 0.1165
65/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9595 - loss: 0.1165
66/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9595 - loss: 0.1165
67/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9596 - loss: 0.1165
68/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9596 - loss: 0.1166
69/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9596 - loss: 0.1166
70/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9596 - loss: 0.1166
71/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9597 - loss: 0.1167
72/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9597 - loss: 0.1167
73/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9597 - loss: 0.1167
74/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9598 - loss: 0.1168
75/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9598 - loss: 0.1168
76/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9598 - loss: 0.1168
77/140 āāāāāāāāāāāāāāāāāāāā 7s 123ms/step - accuracy: 0.9599 - loss: 0.1169
78/140 āāāāāāāāāāāāāāāāāāāā 7s 123ms/step - accuracy: 0.9599 - loss: 0.1169
79/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9599 - loss: 0.1169
80/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9599 - loss: 0.1169
81/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9600 - loss: 0.1169
82/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9600 - loss: 0.1170
83/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9600 - loss: 0.1170
84/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9600 - loss: 0.1170
85/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9601 - loss: 0.1169
86/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9601 - loss: 0.1169
87/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9601 - loss: 0.1169
88/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9602 - loss: 0.1169
89/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9602 - loss: 0.1169
90/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9602 - loss: 0.1169
91/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9602 - loss: 0.1169
92/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9602 - loss: 0.1169
93/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9602 - loss: 0.1169
94/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9603 - loss: 0.1169
95/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9603 - loss: 0.1169
96/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9603 - loss: 0.1168
97/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9603 - loss: 0.1168
98/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9603 - loss: 0.1168
99/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9603 - loss: 0.1168
100/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9603 - loss: 0.1168
101/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9603 - loss: 0.1168
102/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9603 - loss: 0.1169
103/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9603 - loss: 0.1169
104/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9603 - loss: 0.1169
105/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9603 - loss: 0.1169
106/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9603 - loss: 0.1169
107/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9603 - loss: 0.1169
108/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9603 - loss: 0.1169
109/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9603 - loss: 0.1170
110/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9603 - loss: 0.1170
111/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9603 - loss: 0.1170
112/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9603 - loss: 0.1170
113/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9603 - loss: 0.1170
114/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9603 - loss: 0.1170
115/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9603 - loss: 0.1170
116/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9603 - loss: 0.1171
117/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9603 - loss: 0.1171
118/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9603 - loss: 0.1171
119/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9603 - loss: 0.1171
120/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9603 - loss: 0.1171
121/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9603 - loss: 0.1171
122/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9603 - loss: 0.1172
123/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9603 - loss: 0.1172
124/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.9603 - loss: 0.1172
125/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.9602 - loss: 0.1172
126/140 āāāāāāāāāāāāāāāāāāāā 1s 123ms/step - accuracy: 0.9602 - loss: 0.1172
127/140 āāāāāāāāāāāāāāāāāāāā 1s 123ms/step - accuracy: 0.9602 - loss: 0.1173
128/140 āāāāāāāāāāāāāāāāāāāā 1s 123ms/step - accuracy: 0.9602 - loss: 0.1173
129/140 āāāāāāāāāāāāāāāāāāāā 1s 123ms/step - accuracy: 0.9602 - loss: 0.1173
130/140 āāāāāāāāāāāāāāāāāāāā 1s 123ms/step - accuracy: 0.9602 - loss: 0.1173
131/140 āāāāāāāāāāāāāāāāāāāā 1s 123ms/step - accuracy: 0.9602 - loss: 0.1174
132/140 āāāāāāāāāāāāāāāāāāāā 0s 123ms/step - accuracy: 0.9602 - loss: 0.1174
133/140 āāāāāāāāāāāāāāāāāāāā 0s 123ms/step - accuracy: 0.9601 - loss: 0.1174
134/140 āāāāāāāāāāāāāāāāāāāā 0s 123ms/step - accuracy: 0.9601 - loss: 0.1175
135/140 āāāāāāāāāāāāāāāāāāāā 0s 123ms/step - accuracy: 0.9601 - loss: 0.1175
136/140 āāāāāāāāāāāāāāāāāāāā 0s 123ms/step - accuracy: 0.9601 - loss: 0.1175
137/140 āāāāāāāāāāāāāāāāāāāā 0s 123ms/step - accuracy: 0.9601 - loss: 0.1176
138/140 āāāāāāāāāāāāāāāāāāāā 0s 123ms/step - accuracy: 0.9600 - loss: 0.1176
139/140 āāāāāāāāāāāāāāāāāāāā 0s 123ms/step - accuracy: 0.9600 - loss: 0.1176
140/140 āāāāāāāāāāāāāāāāāāāā 0s 123ms/step - accuracy: 0.9600 - loss: 0.1177
140/140 āāāāāāāāāāāāāāāāāāāā 19s 135ms/step - accuracy: 0.9600 - loss: 0.1177 - val_accuracy: 0.8410 - val_loss: 0.4187
Epoch 5/5
1/140 āāāāāāāāāāāāāāāāāāāā 18s 131ms/step - accuracy: 1.0000 - loss: 0.0976
2/140 āāāāāāāāāāāāāāāāāāāā 17s 125ms/step - accuracy: 0.9922 - loss: 0.0999
3/140 āāāāāāāāāāāāāāāāāāāā 17s 127ms/step - accuracy: 0.9913 - loss: 0.0959
4/140 āāāāāāāāāāāāāāāāāāāā 17s 131ms/step - accuracy: 0.9896 - loss: 0.0925
5/140 āāāāāāāāāāāāāāāāāāāā 17s 129ms/step - accuracy: 0.9892 - loss: 0.0887
6/140 āāāāāāāāāāāāāāāāāāāā 17s 128ms/step - accuracy: 0.9884 - loss: 0.0867
7/140 āāāāāāāāāāāāāāāāāāāā 16s 127ms/step - accuracy: 0.9881 - loss: 0.0848
8/140 āāāāāāāāāāāāāāāāāāāā 16s 127ms/step - accuracy: 0.9881 - loss: 0.0831
9/140 āāāāāāāāāāāāāāāāāāāā 16s 126ms/step - accuracy: 0.9871 - loss: 0.0837
10/140 āāāāāāāāāāāāāāāāāāāā 16s 126ms/step - accuracy: 0.9859 - loss: 0.0850
11/140 āāāāāāāāāāāāāāāāāāāā 16s 126ms/step - accuracy: 0.9849 - loss: 0.0862
12/140 āāāāāāāāāāāāāāāāāāāā 16s 126ms/step - accuracy: 0.9840 - loss: 0.0872
13/140 āāāāāāāāāāāāāāāāāāāā 16s 126ms/step - accuracy: 0.9830 - loss: 0.0880
14/140 āāāāāāāāāāāāāāāāāāāā 15s 126ms/step - accuracy: 0.9823 - loss: 0.0884
15/140 āāāāāāāāāāāāāāāāāāāā 15s 126ms/step - accuracy: 0.9817 - loss: 0.0888
16/140 āāāāāāāāāāāāāāāāāāāā 15s 126ms/step - accuracy: 0.9811 - loss: 0.0890
17/140 āāāāāāāāāāāāāāāāāāāā 15s 126ms/step - accuracy: 0.9807 - loss: 0.0892
18/140 āāāāāāāāāāāāāāāāāāāā 15s 126ms/step - accuracy: 0.9803 - loss: 0.0893
19/140 āāāāāāāāāāāāāāāāāāāā 15s 125ms/step - accuracy: 0.9800 - loss: 0.0894
20/140 āāāāāāāāāāāāāāāāāāāā 15s 125ms/step - accuracy: 0.9797 - loss: 0.0895
21/140 āāāāāāāāāāāāāāāāāāāā 14s 125ms/step - accuracy: 0.9793 - loss: 0.0897
22/140 āāāāāāāāāāāāāāāāāāāā 14s 125ms/step - accuracy: 0.9791 - loss: 0.0897
23/140 āāāāāāāāāāāāāāāāāāāā 14s 125ms/step - accuracy: 0.9788 - loss: 0.0898
24/140 āāāāāāāāāāāāāāāāāāāā 14s 125ms/step - accuracy: 0.9785 - loss: 0.0898
25/140 āāāāāāāāāāāāāāāāāāāā 14s 125ms/step - accuracy: 0.9783 - loss: 0.0897
26/140 āāāāāāāāāāāāāāāāāāāā 14s 125ms/step - accuracy: 0.9781 - loss: 0.0898
27/140 āāāāāāāāāāāāāāāāāāāā 14s 125ms/step - accuracy: 0.9778 - loss: 0.0898
28/140 āāāāāāāāāāāāāāāāāāāā 13s 125ms/step - accuracy: 0.9776 - loss: 0.0899
29/140 āāāāāāāāāāāāāāāāāāāā 13s 125ms/step - accuracy: 0.9773 - loss: 0.0900
30/140 āāāāāāāāāāāāāāāāāāāā 13s 125ms/step - accuracy: 0.9771 - loss: 0.0900
31/140 āāāāāāāāāāāāāāāāāāāā 13s 125ms/step - accuracy: 0.9769 - loss: 0.0902
32/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9767 - loss: 0.0903
33/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9766 - loss: 0.0904
34/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9764 - loss: 0.0905
35/140 āāāāāāāāāāāāāāāāāāāā 13s 124ms/step - accuracy: 0.9762 - loss: 0.0906
36/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.9761 - loss: 0.0907
37/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.9759 - loss: 0.0907
38/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.9758 - loss: 0.0908
39/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.9757 - loss: 0.0908
40/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.9756 - loss: 0.0907
41/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.9755 - loss: 0.0907
42/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.9755 - loss: 0.0907
43/140 āāāāāāāāāāāāāāāāāāāā 12s 124ms/step - accuracy: 0.9754 - loss: 0.0907
44/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9753 - loss: 0.0906
45/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9753 - loss: 0.0906
46/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9752 - loss: 0.0905
47/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9752 - loss: 0.0904
48/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9752 - loss: 0.0904
49/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9752 - loss: 0.0903
50/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9752 - loss: 0.0901
51/140 āāāāāāāāāāāāāāāāāāāā 11s 124ms/step - accuracy: 0.9752 - loss: 0.0900
52/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9752 - loss: 0.0899
53/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9752 - loss: 0.0898
54/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9752 - loss: 0.0897
55/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9752 - loss: 0.0896
56/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9752 - loss: 0.0895
57/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9752 - loss: 0.0894
58/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9752 - loss: 0.0894
59/140 āāāāāāāāāāāāāāāāāāāā 10s 124ms/step - accuracy: 0.9752 - loss: 0.0893
60/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9752 - loss: 0.0892
61/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9752 - loss: 0.0891
62/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9752 - loss: 0.0890
63/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9752 - loss: 0.0889
64/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9752 - loss: 0.0889
65/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9752 - loss: 0.0888
66/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9752 - loss: 0.0887
67/140 āāāāāāāāāāāāāāāāāāāā 9s 124ms/step - accuracy: 0.9753 - loss: 0.0886
68/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9753 - loss: 0.0885
69/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9753 - loss: 0.0884
70/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9753 - loss: 0.0884
71/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9753 - loss: 0.0883
72/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9753 - loss: 0.0882
73/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9753 - loss: 0.0881
74/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9753 - loss: 0.0881
75/140 āāāāāāāāāāāāāāāāāāāā 8s 124ms/step - accuracy: 0.9753 - loss: 0.0880
76/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9753 - loss: 0.0879
77/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9754 - loss: 0.0878
78/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9754 - loss: 0.0877
79/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9754 - loss: 0.0876
80/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9754 - loss: 0.0876
81/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9754 - loss: 0.0875
82/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9754 - loss: 0.0874
83/140 āāāāāāāāāāāāāāāāāāāā 7s 124ms/step - accuracy: 0.9754 - loss: 0.0874
84/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9754 - loss: 0.0873
85/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9754 - loss: 0.0872
86/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9754 - loss: 0.0871
87/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9755 - loss: 0.0870
88/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9755 - loss: 0.0869
89/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9755 - loss: 0.0869
90/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9755 - loss: 0.0868
91/140 āāāāāāāāāāāāāāāāāāāā 6s 124ms/step - accuracy: 0.9755 - loss: 0.0867
92/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9755 - loss: 0.0866
93/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9755 - loss: 0.0865
94/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9755 - loss: 0.0865
95/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9755 - loss: 0.0864
96/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9756 - loss: 0.0863
97/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9756 - loss: 0.0863
98/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9756 - loss: 0.0862
99/140 āāāāāāāāāāāāāāāāāāāā 5s 124ms/step - accuracy: 0.9756 - loss: 0.0862
100/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9756 - loss: 0.0861
101/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9756 - loss: 0.0861
102/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9756 - loss: 0.0860
103/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9756 - loss: 0.0860
104/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9756 - loss: 0.0860
105/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9756 - loss: 0.0860
106/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9755 - loss: 0.0859
107/140 āāāāāāāāāāāāāāāāāāāā 4s 124ms/step - accuracy: 0.9755 - loss: 0.0859
108/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9755 - loss: 0.0859
109/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9755 - loss: 0.0859
110/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9755 - loss: 0.0858
111/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9755 - loss: 0.0858
112/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9755 - loss: 0.0858
113/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9754 - loss: 0.0858
114/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9754 - loss: 0.0858
115/140 āāāāāāāāāāāāāāāāāāāā 3s 124ms/step - accuracy: 0.9754 - loss: 0.0858
116/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9754 - loss: 0.0858
117/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9753 - loss: 0.0858
118/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9753 - loss: 0.0858
119/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9753 - loss: 0.0858
120/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9752 - loss: 0.0858
121/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9752 - loss: 0.0858
122/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9752 - loss: 0.0858
123/140 āāāāāāāāāāāāāāāāāāāā 2s 124ms/step - accuracy: 0.9751 - loss: 0.0858
124/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.9751 - loss: 0.0858
125/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.9751 - loss: 0.0859
126/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.9750 - loss: 0.0859
127/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.9750 - loss: 0.0859
128/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.9750 - loss: 0.0859
129/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.9749 - loss: 0.0859
130/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.9749 - loss: 0.0860
131/140 āāāāāāāāāāāāāāāāāāāā 1s 124ms/step - accuracy: 0.9748 - loss: 0.0860
132/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.9748 - loss: 0.0860
133/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.9748 - loss: 0.0860
134/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.9747 - loss: 0.0861
135/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.9747 - loss: 0.0861
136/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.9746 - loss: 0.0861
137/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.9746 - loss: 0.0861
138/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.9746 - loss: 0.0862
139/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.9745 - loss: 0.0862
140/140 āāāāāāāāāāāāāāāāāāāā 0s 124ms/step - accuracy: 0.9745 - loss: 0.0862
140/140 āāāāāāāāāāāāāāāāāāāā 19s 135ms/step - accuracy: 0.9745 - loss: 0.0862 - val_accuracy: 0.8464 - val_loss: 0.4660
1/47 āāāāāāāāāāāāāāāāāāāā 5s 120ms/step
3/47 āāāāāāāāāāāāāāāāāāāā 1s 31ms/step
5/47 āāāāāāāāāāāāāāāāāāāā 1s 31ms/step
7/47 āāāāāāāāāāāāāāāāāāāā 1s 31ms/step
9/47 āāāāāāāāāāāāāāāāāāāā 1s 31ms/step
11/47 āāāāāāāāāāāāāāāāāāāā 1s 32ms/step
13/47 āāāāāāāāāāāāāāāāāāāā 1s 32ms/step
15/47 āāāāāāāāāāāāāāāāāāāā 1s 33ms/step
17/47 āāāāāāāāāāāāāāāāāāāā 0s 33ms/step
19/47 āāāāāāāāāāāāāāāāāāāā 0s 33ms/step
21/47 āāāāāāāāāāāāāāāāāāāā 0s 33ms/step
23/47 āāāāāāāāāāāāāāāāāāāā 0s 33ms/step
25/47 āāāāāāāāāāāāāāāāāāāā 0s 34ms/step
27/47 āāāāāāāāāāāāāāāāāāāā 0s 34ms/step
29/47 āāāāāāāāāāāāāāāāāāāā 0s 34ms/step
31/47 āāāāāāāāāāāāāāāāāāāā 0s 34ms/step
33/47 āāāāāāāāāāāāāāāāāāāā 0s 34ms/step
35/47 āāāāāāāāāāāāāāāāāāāā 0s 34ms/step
37/47 āāāāāāāāāāāāāāāāāāāā 0s 34ms/step
39/47 āāāāāāāāāāāāāāāāāāāā 0s 34ms/step
41/47 āāāāāāāāāāāāāāāāāāāā 0s 33ms/step
43/47 āāāāāāāāāāāāāāāāāāāā 0s 34ms/step
45/47 āāāāāāāāāāāāāāāāāāāā 0s 33ms/step
47/47 āāāāāāāāāāāāāāāāāāāā 0s 35ms/step
47/47 āāāāāāāāāāāāāāāāāāāā 2s 35ms/step
Accuracy: 0.8463611859838275 Precision: 0.8585434173669467 Recall: 0.8283783783783784 Average accuracy: 0.8528584971215846 Average precision: 0.8642070457434622 Average recall: 0.8476473571739612
InĀ [30]:
from tensorflow.keras.layers import Conv1D, GlobalMaxPooling1D
InĀ [31]:
accuracy = []
precision = []
recall = []
for dataset in chosen_datasets:
df = pd.read_csv(os.path.join("datasets", dataset))
X_train, X_test, y_train, y_test = train_test_split(df["text"].values, df["label"].values, test_size=0.25, random_state=1)
tokenizer = Tokenizer()
tokenizer.fit_on_texts(X_train)
sequences = tokenizer.texts_to_sequences(X_train)
max_length = max([len(seq) for seq in sequences])
padded_sequences = pad_sequences(sequences, maxlen=max_length)
new_sequences = tokenizer.texts_to_sequences(X_test)
new_padded_sequences = pad_sequences(new_sequences, maxlen=max_length)
model = Sequential()
model.add(Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=32))
model.add(Conv1D(128, 5, activation='relu'))
model.add(GlobalMaxPooling1D())
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
epochs = 5
history = model.fit(padded_sequences, y_train, validation_data=(new_padded_sequences, y_test), epochs=epochs)
print_metrics(history, epochs=epochs)
predictions = model.predict(new_padded_sequences)
correct = 0
predicted_labels = []
for i in range(len(predictions)):
prediction_label = 1 if predictions[i] > 0.5 else 0
predicted_labels.append(prediction_label)
if y_test[i] == prediction_label:
correct += 1
print_conf_matrix(y_test, predicted_labels)
accuracy.append(correct / len(predictions))
print(f"Accuracy: {correct / len(predictions)}")
print(f"Precision: {precision_score(y_test, predicted_labels)}")
precision.append(precision_score(y_test, predicted_labels))
print(f"Recall: {recall_score(y_test, predicted_labels)}")
recall.append(recall_score(y_test, predicted_labels))
print(f"Average accuracy: {np.average(accuracy)}")
print(f"Average precision: {np.average(precision)}")
print(f"Average recall: {np.average(recall)}")
Epoch 1/5
1/60 āāāāāāāāāāāāāāāāāāāā 33s 573ms/step - accuracy: 0.3438 - loss: 0.6993
3/60 āāāāāāāāāāāāāāāāāāāā 1s 30ms/step - accuracy: 0.4271 - loss: 0.6967
5/60 āāāāāāāāāāāāāāāāāāāā 1s 29ms/step - accuracy: 0.4559 - loss: 0.6954
7/60 āāāāāāāāāāāāāāāāāāāā 1s 30ms/step - accuracy: 0.4657 - loss: 0.6948
9/60 āāāāāāāāāāāāāāāāāāāā 1s 29ms/step - accuracy: 0.4741 - loss: 0.6941
11/60 āāāāāāāāāāāāāāāāāāāā 1s 29ms/step - accuracy: 0.4810 - loss: 0.6935
13/60 āāāāāāāāāāāāāāāāāāāā 1s 29ms/step - accuracy: 0.4872 - loss: 0.6929
15/60 āāāāāāāāāāāāāāāāāāāā 1s 29ms/step - accuracy: 0.4922 - loss: 0.6924
17/60 āāāāāāāāāāāāāāāāāāāā 1s 28ms/step - accuracy: 0.4950 - loss: 0.6921
19/60 āāāāāāāāāāāāāāāāāāāā 1s 29ms/step - accuracy: 0.4970 - loss: 0.6918
21/60 āāāāāāāāāāāāāāāāāāāā 1s 29ms/step - accuracy: 0.4984 - loss: 0.6916
23/60 āāāāāāāāāāāāāāāāāāāā 1s 28ms/step - accuracy: 0.4987 - loss: 0.6914
25/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.4985 - loss: 0.6913
27/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.4984 - loss: 0.6912
29/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.4996 - loss: 0.6910
31/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.5019 - loss: 0.6908
33/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.5049 - loss: 0.6906
35/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.5081 - loss: 0.6903
37/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.5113 - loss: 0.6900
39/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.5140 - loss: 0.6897
41/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.5165 - loss: 0.6893
43/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.5189 - loss: 0.6889
45/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.5210 - loss: 0.6885
47/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.5229 - loss: 0.6880
49/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.5247 - loss: 0.6876
51/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.5265 - loss: 0.6870
53/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.5284 - loss: 0.6865
55/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.5304 - loss: 0.6859
57/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.5325 - loss: 0.6852
59/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.5349 - loss: 0.6845
60/60 āāāāāāāāāāāāāāāāāāāā 2s 31ms/step - accuracy: 0.5372 - loss: 0.6837 - val_accuracy: 0.8454 - val_loss: 0.5547
Epoch 2/5
1/60 āāāāāāāāāāāāāāāāāāāā 2s 36ms/step - accuracy: 0.8750 - loss: 0.5543
3/60 āāāāāāāāāāāāāāāāāāāā 1s 26ms/step - accuracy: 0.8663 - loss: 0.5500
5/60 āāāāāāāāāāāāāāāāāāāā 1s 26ms/step - accuracy: 0.8582 - loss: 0.5454
7/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.8540 - loss: 0.5441
9/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.8502 - loss: 0.5417
11/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.8479 - loss: 0.5394
13/60 āāāāāāāāāāāāāāāāāāāā 1s 26ms/step - accuracy: 0.8469 - loss: 0.5360
15/60 āāāāāāāāāāāāāāāāāāāā 1s 26ms/step - accuracy: 0.8464 - loss: 0.5321
17/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.8470 - loss: 0.5278
19/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.8477 - loss: 0.5235
21/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.8487 - loss: 0.5192
23/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8496 - loss: 0.5151
25/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8504 - loss: 0.5112
27/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8511 - loss: 0.5074
29/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8517 - loss: 0.5036
31/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8519 - loss: 0.5002
33/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8522 - loss: 0.4968
35/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8524 - loss: 0.4938
37/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8526 - loss: 0.4907
39/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8528 - loss: 0.4878
41/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8528 - loss: 0.4851
43/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8526 - loss: 0.4826
45/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8525 - loss: 0.4800
47/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8524 - loss: 0.4775
49/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8525 - loss: 0.4750
51/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8525 - loss: 0.4726
53/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8525 - loss: 0.4703
55/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8525 - loss: 0.4681
57/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8525 - loss: 0.4659
59/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.8525 - loss: 0.4640
60/60 āāāāāāāāāāāāāāāāāāāā 2s 29ms/step - accuracy: 0.8524 - loss: 0.4621 - val_accuracy: 0.8675 - val_loss: 0.3358
Epoch 3/5
1/60 āāāāāāāāāāāāāāāāāāāā 2s 36ms/step - accuracy: 0.9375 - loss: 0.2543
3/60 āāāāāāāāāāāāāāāāāāāā 1s 28ms/step - accuracy: 0.9201 - loss: 0.2825
5/60 āāāāāāāāāāāāāāāāāāāā 1s 28ms/step - accuracy: 0.9146 - loss: 0.2807
7/60 āāāāāāāāāāāāāāāāāāāā 1s 28ms/step - accuracy: 0.9081 - loss: 0.2797
9/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.9064 - loss: 0.2768
11/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.9053 - loss: 0.2742
13/60 āāāāāāāāāāāāāāāāāāāā 1s 28ms/step - accuracy: 0.9032 - loss: 0.2727
15/60 āāāāāāāāāāāāāāāāāāāā 1s 28ms/step - accuracy: 0.9021 - loss: 0.2711
17/60 āāāāāāāāāāāāāāāāāāāā 1s 28ms/step - accuracy: 0.9018 - loss: 0.2698
19/60 āāāāāāāāāāāāāāāāāāāā 1s 28ms/step - accuracy: 0.9013 - loss: 0.2686
21/60 āāāāāāāāāāāāāāāāāāāā 1s 28ms/step - accuracy: 0.9010 - loss: 0.2678
23/60 āāāāāāāāāāāāāāāāāāāā 1s 28ms/step - accuracy: 0.9007 - loss: 0.2677
25/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.9008 - loss: 0.2675
27/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.9007 - loss: 0.2676
29/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9009 - loss: 0.2672
31/60 āāāāāāāāāāāāāāāāāāāā 0s 28ms/step - accuracy: 0.9012 - loss: 0.2664
33/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9013 - loss: 0.2657
35/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9015 - loss: 0.2648
37/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9016 - loss: 0.2640
39/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9016 - loss: 0.2635
41/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9017 - loss: 0.2628
43/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9018 - loss: 0.2622
45/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9018 - loss: 0.2616
47/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9018 - loss: 0.2610
49/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9019 - loss: 0.2603
51/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9021 - loss: 0.2597
53/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9022 - loss: 0.2590
55/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9023 - loss: 0.2583
57/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9025 - loss: 0.2575
59/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9027 - loss: 0.2568
60/60 āāāāāāāāāāāāāāāāāāāā 2s 29ms/step - accuracy: 0.9030 - loss: 0.2560 - val_accuracy: 0.8880 - val_loss: 0.2807
Epoch 4/5
1/60 āāāāāāāāāāāāāāāāāāāā 2s 37ms/step - accuracy: 0.9688 - loss: 0.1455
3/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.9688 - loss: 0.1406
5/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.9631 - loss: 0.1399
7/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.9620 - loss: 0.1389
9/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.9594 - loss: 0.1389
11/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.9571 - loss: 0.1395
13/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.9562 - loss: 0.1388
15/60 āāāāāāāāāāāāāāāāāāāā 1s 26ms/step - accuracy: 0.9560 - loss: 0.1380
17/60 āāāāāāāāāāāāāāāāāāāā 1s 26ms/step - accuracy: 0.9555 - loss: 0.1380
19/60 āāāāāāāāāāāāāāāāāāāā 1s 26ms/step - accuracy: 0.9550 - loss: 0.1384
21/60 āāāāāāāāāāāāāāāāāāāā 1s 26ms/step - accuracy: 0.9550 - loss: 0.1383
23/60 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9551 - loss: 0.1381
25/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9553 - loss: 0.1379
27/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9556 - loss: 0.1376
29/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9559 - loss: 0.1374
31/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9563 - loss: 0.1369
33/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9568 - loss: 0.1363
35/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9572 - loss: 0.1357
37/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9577 - loss: 0.1350
39/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9581 - loss: 0.1343
41/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9585 - loss: 0.1337
43/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9588 - loss: 0.1332
45/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9590 - loss: 0.1328
47/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9592 - loss: 0.1325
49/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9594 - loss: 0.1321
51/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9596 - loss: 0.1317
53/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9597 - loss: 0.1313
55/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9599 - loss: 0.1309
57/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9600 - loss: 0.1306
59/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9600 - loss: 0.1302
60/60 āāāāāāāāāāāāāāāāāāāā 2s 29ms/step - accuracy: 0.9601 - loss: 0.1299 - val_accuracy: 0.8991 - val_loss: 0.2723
Epoch 5/5
1/60 āāāāāāāāāāāāāāāāāāāā 2s 40ms/step - accuracy: 1.0000 - loss: 0.0724
3/60 āāāāāāāāāāāāāāāāāāāā 1s 28ms/step - accuracy: 0.9878 - loss: 0.0769
5/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.9871 - loss: 0.0785
7/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.9880 - loss: 0.0777
9/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.9890 - loss: 0.0757
11/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.9899 - loss: 0.0737
13/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.9903 - loss: 0.0725
15/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.9905 - loss: 0.0718
17/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.9907 - loss: 0.0710
19/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.9910 - loss: 0.0702
21/60 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.9912 - loss: 0.0694
23/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9911 - loss: 0.0689
25/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9910 - loss: 0.0684
27/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9910 - loss: 0.0679
29/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9910 - loss: 0.0674
31/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9910 - loss: 0.0670
33/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9910 - loss: 0.0666
35/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9910 - loss: 0.0661
37/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9911 - loss: 0.0656
39/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9911 - loss: 0.0652
41/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9911 - loss: 0.0648
43/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9912 - loss: 0.0645
45/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9912 - loss: 0.0641
47/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9912 - loss: 0.0638
49/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9912 - loss: 0.0635
51/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9912 - loss: 0.0633
53/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9912 - loss: 0.0630
55/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9913 - loss: 0.0627
57/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9913 - loss: 0.0624
59/60 āāāāāāāāāāāāāāāāāāāā 0s 27ms/step - accuracy: 0.9913 - loss: 0.0622
60/60 āāāāāāāāāāāāāāāāāāāā 2s 29ms/step - accuracy: 0.9913 - loss: 0.0621 - val_accuracy: 0.9006 - val_loss: 0.2862
1/20 āāāāāāāāāāāāāāāāāāāā 0s 37ms/step
9/20 āāāāāāāāāāāāāāāāāāāā 0s 7ms/step
18/20 āāāāāāāāāāāāāāāāāāāā 0s 6ms/step
20/20 āāāāāāāāāāāāāāāāāāāā 0s 7ms/step
Accuracy: 0.9006309148264984 Precision: 0.9169435215946844 Recall: 0.8789808917197452
Epoch 1/5
1/67 āāāāāāāāāāāāāāāāāāāā 37s 564ms/step - accuracy: 0.5000 - loss: 0.6938
3/67 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.5365 - loss: 0.6934
6/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.5484 - loss: 0.6925
9/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.5493 - loss: 0.6917
12/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.5481 - loss: 0.6911
15/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.5463 - loss: 0.6907
18/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.5427 - loss: 0.6905
21/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.5381 - loss: 0.6905
24/67 āāāāāāāāāāāāāāāāāāāā 1s 24ms/step - accuracy: 0.5358 - loss: 0.6904
27/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.5361 - loss: 0.6902
30/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.5369 - loss: 0.6899
33/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.5375 - loss: 0.6896
36/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.5379 - loss: 0.6893
39/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.5382 - loss: 0.6889
42/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.5387 - loss: 0.6885
45/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.5394 - loss: 0.6881
48/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.5406 - loss: 0.6876
51/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.5421 - loss: 0.6871
54/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.5439 - loss: 0.6865
57/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.5460 - loss: 0.6859
60/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.5482 - loss: 0.6853
63/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.5505 - loss: 0.6846
66/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.5528 - loss: 0.6839
67/67 āāāāāāāāāāāāāāāāāāāā 2s 27ms/step - accuracy: 0.5543 - loss: 0.6833 - val_accuracy: 0.6908 - val_loss: 0.6088
Epoch 2/5
1/67 āāāāāāāāāāāāāāāāāāāā 2s 34ms/step - accuracy: 0.6562 - loss: 0.6212
3/67 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.7188 - loss: 0.6073
6/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.7105 - loss: 0.6090
8/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.7120 - loss: 0.6052
10/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.7114 - loss: 0.6026
13/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.7095 - loss: 0.6011
15/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.7101 - loss: 0.5994
17/67 āāāāāāāāāāāāāāāāāāāā 1s 26ms/step - accuracy: 0.7120 - loss: 0.5971
20/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.7145 - loss: 0.5938
23/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.7159 - loss: 0.5913
26/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.7171 - loss: 0.5889
28/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7179 - loss: 0.5873
31/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7189 - loss: 0.5850
34/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7197 - loss: 0.5829
36/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7202 - loss: 0.5815
39/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7210 - loss: 0.5794
42/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7214 - loss: 0.5778
44/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7216 - loss: 0.5769
47/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7218 - loss: 0.5756
50/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7221 - loss: 0.5744
53/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7226 - loss: 0.5730
56/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7232 - loss: 0.5716
59/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7238 - loss: 0.5704
62/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7243 - loss: 0.5692
65/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7249 - loss: 0.5680
67/67 āāāāāāāāāāāāāāāāāāāā 2s 26ms/step - accuracy: 0.7256 - loss: 0.5670 - val_accuracy: 0.7759 - val_loss: 0.4822
Epoch 3/5
1/67 āāāāāāāāāāāāāāāāāāāā 2s 34ms/step - accuracy: 0.7500 - loss: 0.5634
4/67 āāāāāāāāāāāāāāāāāāāā 1s 23ms/step - accuracy: 0.7956 - loss: 0.5102
7/67 āāāāāāāāāāāāāāāāāāāā 1s 24ms/step - accuracy: 0.8161 - loss: 0.4853
10/67 āāāāāāāāāāāāāāāāāāāā 1s 24ms/step - accuracy: 0.8203 - loss: 0.4747
13/67 āāāāāāāāāāāāāāāāāāāā 1s 24ms/step - accuracy: 0.8181 - loss: 0.4716
16/67 āāāāāāāāāāāāāāāāāāāā 1s 24ms/step - accuracy: 0.8186 - loss: 0.4673
19/67 āāāāāāāāāāāāāāāāāāāā 1s 24ms/step - accuracy: 0.8184 - loss: 0.4645
22/67 āāāāāāāāāāāāāāāāāāāā 1s 24ms/step - accuracy: 0.8186 - loss: 0.4620
25/67 āāāāāāāāāāāāāāāāāāāā 1s 24ms/step - accuracy: 0.8189 - loss: 0.4595
28/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.8192 - loss: 0.4575
31/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.8198 - loss: 0.4555
34/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.8206 - loss: 0.4536
36/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.8214 - loss: 0.4521
39/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.8224 - loss: 0.4500
41/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.8231 - loss: 0.4487
43/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.8237 - loss: 0.4473
45/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.8243 - loss: 0.4460
48/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.8252 - loss: 0.4440
51/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.8261 - loss: 0.4420
54/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.8268 - loss: 0.4402
57/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.8275 - loss: 0.4383
60/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.8283 - loss: 0.4363
63/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.8291 - loss: 0.4344
66/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.8299 - loss: 0.4326
67/67 āāāāāāāāāāāāāāāāāāāā 2s 26ms/step - accuracy: 0.8304 - loss: 0.4313 - val_accuracy: 0.8525 - val_loss: 0.3551
Epoch 4/5
1/67 āāāāāāāāāāāāāāāāāāāā 2s 33ms/step - accuracy: 0.9375 - loss: 0.2132
4/67 āāāāāāāāāāāāāāāāāāāā 1s 23ms/step - accuracy: 0.9290 - loss: 0.2375
6/67 āāāāāāāāāāāāāāāāāāāā 1s 24ms/step - accuracy: 0.9272 - loss: 0.2461
8/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.9235 - loss: 0.2538
10/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.9204 - loss: 0.2602
12/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.9196 - loss: 0.2628
15/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.9186 - loss: 0.2643
17/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.9185 - loss: 0.2638
20/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.9184 - loss: 0.2630
22/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.9185 - loss: 0.2621
24/67 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.9184 - loss: 0.2612
27/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9185 - loss: 0.2599
30/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9184 - loss: 0.2592
32/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9185 - loss: 0.2584
35/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9184 - loss: 0.2577
38/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9183 - loss: 0.2569
40/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9183 - loss: 0.2563
43/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9183 - loss: 0.2553
45/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9183 - loss: 0.2547
47/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9182 - loss: 0.2541
49/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9183 - loss: 0.2535
52/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9183 - loss: 0.2526
55/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9184 - loss: 0.2518
57/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9184 - loss: 0.2513
59/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9184 - loss: 0.2507
62/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9184 - loss: 0.2500
64/67 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9184 - loss: 0.2495
67/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.9185 - loss: 0.2488
67/67 āāāāāāāāāāāāāāāāāāāā 2s 26ms/step - accuracy: 0.9185 - loss: 0.2485 - val_accuracy: 0.8879 - val_loss: 0.2816
Epoch 5/5
1/67 āāāāāāāāāāāāāāāāāāāā 2s 34ms/step - accuracy: 1.0000 - loss: 0.0702
4/67 āāāāāāāāāāāāāāāāāāāā 1s 24ms/step - accuracy: 0.9896 - loss: 0.0963
7/67 āāāāāāāāāāāāāāāāāāāā 1s 24ms/step - accuracy: 0.9866 - loss: 0.1099
10/67 āāāāāāāāāāāāāāāāāāāā 1s 24ms/step - accuracy: 0.9851 - loss: 0.1172
13/67 āāāāāāāāāāāāāāāāāāāā 1s 24ms/step - accuracy: 0.9841 - loss: 0.1198
16/67 āāāāāāāāāāāāāāāāāāāā 1s 24ms/step - accuracy: 0.9835 - loss: 0.1206
19/67 āāāāāāāāāāāāāāāāāāāā 1s 24ms/step - accuracy: 0.9829 - loss: 0.1210
21/67 āāāāāāāāāāāāāāāāāāāā 1s 24ms/step - accuracy: 0.9822 - loss: 0.1213
24/67 āāāāāāāāāāāāāāāāāāāā 1s 24ms/step - accuracy: 0.9812 - loss: 0.1217
27/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.9802 - loss: 0.1224
30/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.9794 - loss: 0.1229
33/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.9788 - loss: 0.1233
35/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.9784 - loss: 0.1235
38/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.9781 - loss: 0.1238
41/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.9777 - loss: 0.1240
44/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.9774 - loss: 0.1243
46/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.9771 - loss: 0.1245
49/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.9767 - loss: 0.1248
52/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.9765 - loss: 0.1249
55/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.9763 - loss: 0.1251
58/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.9761 - loss: 0.1250
61/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.9760 - loss: 0.1251
64/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.9757 - loss: 0.1254
67/67 āāāāāāāāāāāāāāāāāāāā 0s 24ms/step - accuracy: 0.9755 - loss: 0.1256
67/67 āāāāāāāāāāāāāāāāāāāā 2s 26ms/step - accuracy: 0.9754 - loss: 0.1257 - val_accuracy: 0.8823 - val_loss: 0.2694
1/23 āāāāāāāāāāāāāāāāāāāā 0s 37ms/step
11/23 āāāāāāāāāāāāāāāāāāāā 0s 5ms/step
21/23 āāāāāāāāāāāāāāāāāāāā 0s 5ms/step
23/23 āāāāāāāāāāāāāāāāāāāā 0s 6ms/step
Accuracy: 0.8822695035460993 Precision: 0.8590425531914894 Recall: 0.9150141643059491
Epoch 1/5
1/53 āāāāāāāāāāāāāāāāāāāā 29s 561ms/step - accuracy: 0.3750 - loss: 0.6958
6/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.4443 - loss: 0.6935
11/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.4703 - loss: 0.6907
16/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.4807 - loss: 0.6878
21/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.4899 - loss: 0.6849
26/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.4998 - loss: 0.6819
31/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.5102 - loss: 0.6789
36/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.5182 - loss: 0.6757
41/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.5256 - loss: 0.6721
46/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.5329 - loss: 0.6684
51/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.5394 - loss: 0.6645
53/53 āāāāāāāāāāāāāāāāāāāā 1s 14ms/step - accuracy: 0.5431 - loss: 0.6619 - val_accuracy: 0.6619 - val_loss: 0.5524
Epoch 2/5
1/53 āāāāāāāāāāāāāāāāāāāā 1s 22ms/step - accuracy: 0.6875 - loss: 0.5354
6/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.6690 - loss: 0.5284
11/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.6668 - loss: 0.5216
16/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.6748 - loss: 0.5199
21/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.6813 - loss: 0.5210
26/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.6873 - loss: 0.5205
31/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.6935 - loss: 0.5199
36/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.6986 - loss: 0.5192
41/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.7036 - loss: 0.5180
46/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.7079 - loss: 0.5163
51/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.7119 - loss: 0.5146
53/53 āāāāāāāāāāāāāāāāāāāā 1s 13ms/step - accuracy: 0.7141 - loss: 0.5137 - val_accuracy: 0.7710 - val_loss: 0.4790
Epoch 3/5
1/53 āāāāāāāāāāāāāāāāāāāā 1s 24ms/step - accuracy: 0.7812 - loss: 0.5212
6/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.8090 - loss: 0.4682
11/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.8052 - loss: 0.4547
16/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.8085 - loss: 0.4451
21/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.8138 - loss: 0.4352
26/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.8179 - loss: 0.4270
30/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.8200 - loss: 0.4222
35/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.8220 - loss: 0.4175
40/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.8244 - loss: 0.4132
45/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.8265 - loss: 0.4096
49/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.8281 - loss: 0.4070
53/53 āāāāāāāāāāāāāāāāāāāā 1s 13ms/step - accuracy: 0.8299 - loss: 0.4042 - val_accuracy: 0.8211 - val_loss: 0.4012
Epoch 4/5
1/53 āāāāāāāāāāāāāāāāāāāā 1s 23ms/step - accuracy: 0.9688 - loss: 0.1893
5/53 āāāāāāāāāāāāāāāāāāāā 0s 13ms/step - accuracy: 0.9157 - loss: 0.2536
10/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9085 - loss: 0.2619
15/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9089 - loss: 0.2625
20/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9102 - loss: 0.2622
24/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9112 - loss: 0.2613
29/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9121 - loss: 0.2600
34/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9124 - loss: 0.2586
39/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9133 - loss: 0.2568
44/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9143 - loss: 0.2550
49/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9152 - loss: 0.2533
53/53 āāāāāāāāāāāāāāāāāāāā 1s 13ms/step - accuracy: 0.9160 - loss: 0.2517 - val_accuracy: 0.8640 - val_loss: 0.3424
Epoch 5/5
1/53 āāāāāāāāāāāāāāāāāāāā 1s 22ms/step - accuracy: 0.9688 - loss: 0.1673
6/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9497 - loss: 0.1692
11/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9547 - loss: 0.1585
16/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9567 - loss: 0.1550
21/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9572 - loss: 0.1543
26/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9580 - loss: 0.1532
31/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9585 - loss: 0.1524
36/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9591 - loss: 0.1512
41/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9595 - loss: 0.1500
46/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9597 - loss: 0.1490
51/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.9600 - loss: 0.1478
53/53 āāāāāāāāāāāāāāāāāāāā 1s 13ms/step - accuracy: 0.9601 - loss: 0.1470 - val_accuracy: 0.8551 - val_loss: 0.3312
1/18 āāāāāāāāāāāāāāāāāāāā 0s 34ms/step
18/18 āāāāāāāāāāāāāāāāāāāā 0s 4ms/step
18/18 āāāāāāāāāāāāāāāāāāāā 0s 4ms/step
Accuracy: 0.855098389982111 Precision: 0.8458781362007168 Recall: 0.8613138686131386
Epoch 1/5
1/48 āāāāāāāāāāāāāāāāāāāā 26s 571ms/step - accuracy: 0.5938 - loss: 0.6790
3/48 āāāāāāāāāāāāāāāāāāāā 1s 28ms/step - accuracy: 0.5556 - loss: 0.6845
5/48 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.5296 - loss: 0.6883
7/48 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.5128 - loss: 0.6907
10/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.5048 - loss: 0.6921
12/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.5002 - loss: 0.6926
14/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.4977 - loss: 0.6929
16/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.4979 - loss: 0.6930
18/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.4994 - loss: 0.6930
20/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.5018 - loss: 0.6929
22/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.5045 - loss: 0.6928
24/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.5061 - loss: 0.6927
26/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.5080 - loss: 0.6926
28/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.5095 - loss: 0.6925
30/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.5112 - loss: 0.6923
32/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.5125 - loss: 0.6922
34/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.5140 - loss: 0.6921
36/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.5156 - loss: 0.6919
38/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.5174 - loss: 0.6917
40/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.5190 - loss: 0.6916
42/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.5207 - loss: 0.6914
44/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.5222 - loss: 0.6912
46/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.5236 - loss: 0.6911
48/48 āāāāāāāāāāāāāāāāāāāā 2s 34ms/step - accuracy: 0.5257 - loss: 0.6908 - val_accuracy: 0.6686 - val_loss: 0.6753
Epoch 2/5
1/48 āāāāāāāāāāāāāāāāāāāā 1s 38ms/step - accuracy: 0.7812 - loss: 0.6785
3/48 āāāāāāāāāāāāāāāāāāāā 1s 26ms/step - accuracy: 0.8125 - loss: 0.6732
5/48 āāāāāāāāāāāāāāāāāāāā 1s 26ms/step - accuracy: 0.8266 - loss: 0.6702
7/48 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.8331 - loss: 0.6672
9/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.8241 - loss: 0.6654
12/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.8158 - loss: 0.6632
14/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.8087 - loss: 0.6620
16/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.8005 - loss: 0.6611
19/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7898 - loss: 0.6595
21/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7835 - loss: 0.6585
23/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7780 - loss: 0.6577
25/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7736 - loss: 0.6568
27/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7706 - loss: 0.6559
29/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7679 - loss: 0.6552
31/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7659 - loss: 0.6546
33/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7644 - loss: 0.6539
35/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7634 - loss: 0.6533
37/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7628 - loss: 0.6526
39/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7624 - loss: 0.6520
41/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7622 - loss: 0.6513
43/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.7621 - loss: 0.6506
45/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.7620 - loss: 0.6499
47/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.7620 - loss: 0.6492
48/48 āāāāāāāāāāāāāāāāāāāā 1s 27ms/step - accuracy: 0.7620 - loss: 0.6485 - val_accuracy: 0.7416 - val_loss: 0.6198
Epoch 3/5
1/48 āāāāāāāāāāāāāāāāāāāā 1s 37ms/step - accuracy: 0.8750 - loss: 0.5716
3/48 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.8698 - loss: 0.5749
5/48 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.8750 - loss: 0.5689
7/48 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step - accuracy: 0.8722 - loss: 0.5691
9/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8724 - loss: 0.5683
11/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8716 - loss: 0.5680
13/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8696 - loss: 0.5678
15/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8686 - loss: 0.5674
17/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8673 - loss: 0.5664
19/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8665 - loss: 0.5652
21/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8657 - loss: 0.5636
23/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8653 - loss: 0.5620
25/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8648 - loss: 0.5603
27/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8642 - loss: 0.5586
29/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8632 - loss: 0.5571
31/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8626 - loss: 0.5554
33/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8621 - loss: 0.5536
35/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8617 - loss: 0.5518
37/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8613 - loss: 0.5501
39/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8610 - loss: 0.5484
41/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8608 - loss: 0.5468
43/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8605 - loss: 0.5451
45/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8603 - loss: 0.5436
47/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.8601 - loss: 0.5420
48/48 āāāāāāāāāāāāāāāāāāāā 1s 28ms/step - accuracy: 0.8600 - loss: 0.5404 - val_accuracy: 0.7712 - val_loss: 0.4968
Epoch 4/5
1/48 āāāāāāāāāāāāāāāāāāāā 1s 37ms/step - accuracy: 0.8750 - loss: 0.4570
3/48 āāāāāāāāāāāāāāāāāāāā 1s 26ms/step - accuracy: 0.9028 - loss: 0.4403
5/48 āāāāāāāāāāāāāāāāāāāā 1s 26ms/step - accuracy: 0.9054 - loss: 0.4294
7/48 āāāāāāāāāāāāāāāāāāāā 1s 26ms/step - accuracy: 0.9097 - loss: 0.4209
9/48 āāāāāāāāāāāāāāāāāāāā 1s 26ms/step - accuracy: 0.9151 - loss: 0.4109
11/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9172 - loss: 0.4026
13/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9183 - loss: 0.3958
15/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9190 - loss: 0.3902
17/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9187 - loss: 0.3857
19/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9179 - loss: 0.3815
21/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9170 - loss: 0.3782
24/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9158 - loss: 0.3740
26/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9155 - loss: 0.3712
28/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9153 - loss: 0.3687
30/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9153 - loss: 0.3662
33/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9152 - loss: 0.3626
35/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9151 - loss: 0.3604
37/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9151 - loss: 0.3582
39/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9150 - loss: 0.3561
41/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9151 - loss: 0.3541
43/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9152 - loss: 0.3520
45/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9152 - loss: 0.3501
47/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9152 - loss: 0.3484
48/48 āāāāāāāāāāāāāāāāāāāā 1s 28ms/step - accuracy: 0.9152 - loss: 0.3466 - val_accuracy: 0.8067 - val_loss: 0.4197
Epoch 5/5
1/48 āāāāāāāāāāāāāāāāāāāā 1s 37ms/step - accuracy: 0.9688 - loss: 0.1920
3/48 āāāāāāāāāāāāāāāāāāāā 1s 26ms/step - accuracy: 0.9635 - loss: 0.1889
5/48 āāāāāāāāāāāāāāāāāāāā 1s 26ms/step - accuracy: 0.9656 - loss: 0.1803
7/48 āāāāāāāāāāāāāāāāāāāā 1s 26ms/step - accuracy: 0.9645 - loss: 0.1766
9/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9638 - loss: 0.1742
11/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9636 - loss: 0.1720
13/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9633 - loss: 0.1716
15/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9628 - loss: 0.1726
17/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9628 - loss: 0.1729
20/48 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step - accuracy: 0.9625 - loss: 0.1737
22/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9626 - loss: 0.1737
24/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9628 - loss: 0.1734
26/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9631 - loss: 0.1730
28/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9633 - loss: 0.1727
30/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9632 - loss: 0.1726
32/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9630 - loss: 0.1726
35/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9627 - loss: 0.1725
37/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9625 - loss: 0.1721
39/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9625 - loss: 0.1716
41/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9624 - loss: 0.1712
43/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9624 - loss: 0.1708
45/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9623 - loss: 0.1704
48/48 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step - accuracy: 0.9621 - loss: 0.1697
48/48 āāāāāāāāāāāāāāāāāāāā 1s 28ms/step - accuracy: 0.9621 - loss: 0.1695 - val_accuracy: 0.7929 - val_loss: 0.4212
1/16 āāāāāāāāāāāāāāāāāāāā 0s 39ms/step
10/16 āāāāāāāāāāāāāāāāāāāā 0s 6ms/step
16/16 āāāāāāāāāāāāāāāāāāāā 0s 7ms/step
16/16 āāāāāāāāāāāāāāāāāāāā 0s 7ms/step
Accuracy: 0.7928994082840237 Precision: 0.807843137254902 Recall: 0.7862595419847328
Epoch 1/5
1/140 āāāāāāāāāāāāāāāāāāāā 1:18 565ms/step - accuracy: 0.3750 - loss: 0.6968
4/140 āāāāāāāāāāāāāāāāāāāā 2s 19ms/step - accuracy: 0.4290 - loss: 0.6956
7/140 āāāāāāāāāāāāāāāāāāāā 2s 19ms/step - accuracy: 0.4587 - loss: 0.6947
10/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.4728 - loss: 0.6941
13/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.4774 - loss: 0.6941
16/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.4772 - loss: 0.6946
19/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.4771 - loss: 0.6948
22/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.4768 - loss: 0.6949
25/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.4780 - loss: 0.6949
28/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.4796 - loss: 0.6948
31/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.4814 - loss: 0.6948
34/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.4836 - loss: 0.6946
37/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.4853 - loss: 0.6945
40/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.4867 - loss: 0.6944
43/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.4880 - loss: 0.6942
46/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.4895 - loss: 0.6940
49/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.4908 - loss: 0.6939
52/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.4919 - loss: 0.6937
55/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.4931 - loss: 0.6935
58/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.4944 - loss: 0.6933
61/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.4961 - loss: 0.6931
64/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.4979 - loss: 0.6929
67/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.4998 - loss: 0.6928
70/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.5019 - loss: 0.6925
73/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.5041 - loss: 0.6923
76/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.5062 - loss: 0.6921
79/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.5081 - loss: 0.6919
82/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.5100 - loss: 0.6916
85/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5119 - loss: 0.6913
88/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5137 - loss: 0.6910
91/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5156 - loss: 0.6907
94/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5176 - loss: 0.6904
97/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5197 - loss: 0.6900
100/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5218 - loss: 0.6896
103/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5238 - loss: 0.6892
106/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5259 - loss: 0.6887
109/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5279 - loss: 0.6883
112/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5300 - loss: 0.6878
115/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5320 - loss: 0.6873
118/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5341 - loss: 0.6868
121/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5362 - loss: 0.6862
124/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5382 - loss: 0.6857
127/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5403 - loss: 0.6851
130/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5423 - loss: 0.6844
133/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5443 - loss: 0.6838
136/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5463 - loss: 0.6831
139/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.5482 - loss: 0.6823
140/140 āāāāāāāāāāāāāāāāāāāā 3s 20ms/step - accuracy: 0.5495 - loss: 0.6818 - val_accuracy: 0.7642 - val_loss: 0.5144
Epoch 2/5
1/140 āāāāāāāāāāāāāāāāāāāā 3s 28ms/step - accuracy: 0.9375 - loss: 0.4247
4/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.8978 - loss: 0.4404
7/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.8772 - loss: 0.4534
10/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.8705 - loss: 0.4584
13/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.8654 - loss: 0.4614
16/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.8605 - loss: 0.4643
19/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.8576 - loss: 0.4651
22/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.8553 - loss: 0.4646
25/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.8538 - loss: 0.4633
28/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.8522 - loss: 0.4620
31/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.8508 - loss: 0.4607
34/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.8498 - loss: 0.4595
37/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.8490 - loss: 0.4582
40/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.8484 - loss: 0.4566
43/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.8480 - loss: 0.4548
46/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.8476 - loss: 0.4531
49/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.8470 - loss: 0.4515
52/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.8466 - loss: 0.4498
55/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.8461 - loss: 0.4483
58/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.8458 - loss: 0.4468
61/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.8456 - loss: 0.4453
64/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.8454 - loss: 0.4437
67/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.8452 - loss: 0.4423
70/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.8449 - loss: 0.4408
73/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.8447 - loss: 0.4394
76/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.8445 - loss: 0.4380
79/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.8444 - loss: 0.4366
82/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.8443 - loss: 0.4352
85/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8442 - loss: 0.4338
88/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8441 - loss: 0.4325
91/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8439 - loss: 0.4313
94/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8437 - loss: 0.4301
97/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8436 - loss: 0.4290
100/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8434 - loss: 0.4278
103/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8433 - loss: 0.4267
106/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8432 - loss: 0.4256
109/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8431 - loss: 0.4245
112/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8430 - loss: 0.4234
115/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8430 - loss: 0.4223
118/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8430 - loss: 0.4212
121/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8430 - loss: 0.4202
124/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8430 - loss: 0.4192
127/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8430 - loss: 0.4182
130/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8430 - loss: 0.4172
133/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8430 - loss: 0.4163
136/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8430 - loss: 0.4154
139/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.8430 - loss: 0.4146
140/140 āāāāāāāāāāāāāāāāāāāā 3s 19ms/step - accuracy: 0.8430 - loss: 0.4141 - val_accuracy: 0.8201 - val_loss: 0.3857
Epoch 3/5
1/140 āāāāāāāāāāāāāāāāāāāā 3s 28ms/step - accuracy: 0.8750 - loss: 0.2304
4/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.8997 - loss: 0.2196
7/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9114 - loss: 0.2126
10/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9178 - loss: 0.2087
13/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9211 - loss: 0.2069
16/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9247 - loss: 0.2053
19/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9266 - loss: 0.2043
22/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9284 - loss: 0.2029
25/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9295 - loss: 0.2021
28/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9304 - loss: 0.2015
31/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9310 - loss: 0.2015
34/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9312 - loss: 0.2016
37/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9314 - loss: 0.2017
40/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9313 - loss: 0.2024
43/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9307 - loss: 0.2035
46/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9302 - loss: 0.2045
49/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9298 - loss: 0.2053
52/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9295 - loss: 0.2058
55/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9293 - loss: 0.2060
58/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9291 - loss: 0.2063
61/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9290 - loss: 0.2065
64/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9289 - loss: 0.2065
67/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9289 - loss: 0.2065
70/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9289 - loss: 0.2064
73/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9290 - loss: 0.2061
76/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9291 - loss: 0.2058
79/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9291 - loss: 0.2057
82/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9292 - loss: 0.2055
85/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9291 - loss: 0.2055
88/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9291 - loss: 0.2054
91/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9291 - loss: 0.2053
94/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9291 - loss: 0.2052
97/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9290 - loss: 0.2051
100/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9290 - loss: 0.2050
103/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9290 - loss: 0.2048
106/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9290 - loss: 0.2047
109/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9289 - loss: 0.2045
112/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9289 - loss: 0.2045
115/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9287 - loss: 0.2045
118/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9287 - loss: 0.2044
121/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9286 - loss: 0.2042
124/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9285 - loss: 0.2041
127/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9285 - loss: 0.2040
130/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9284 - loss: 0.2038
133/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9283 - loss: 0.2037
136/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9283 - loss: 0.2036
139/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9282 - loss: 0.2035
140/140 āāāāāāāāāāāāāāāāāāāā 3s 19ms/step - accuracy: 0.9281 - loss: 0.2033 - val_accuracy: 0.8336 - val_loss: 0.3840
Epoch 4/5
1/140 āāāāāāāāāāāāāāāāāāāā 3s 28ms/step - accuracy: 1.0000 - loss: 0.0949
4/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9850 - loss: 0.0989
7/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9781 - loss: 0.1019
10/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9736 - loss: 0.1054
13/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9714 - loss: 0.1081
16/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9705 - loss: 0.1088
19/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9701 - loss: 0.1086
22/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9697 - loss: 0.1080
25/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9694 - loss: 0.1074
28/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9693 - loss: 0.1066
31/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9694 - loss: 0.1056
34/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9696 - loss: 0.1046
37/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9697 - loss: 0.1038
40/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9697 - loss: 0.1032
43/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9697 - loss: 0.1026
46/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9696 - loss: 0.1025
49/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9695 - loss: 0.1023
52/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9694 - loss: 0.1021
55/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9694 - loss: 0.1020
58/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9694 - loss: 0.1018
61/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9694 - loss: 0.1016
64/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9693 - loss: 0.1015
67/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9693 - loss: 0.1014
70/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9693 - loss: 0.1012
73/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9694 - loss: 0.1011
76/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9694 - loss: 0.1009
79/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9694 - loss: 0.1008
82/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9694 - loss: 0.1006
85/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9694 - loss: 0.1005
88/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9694 - loss: 0.1004
91/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9694 - loss: 0.1003
94/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9695 - loss: 0.1001
97/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9695 - loss: 0.1000
100/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9695 - loss: 0.0998
103/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9696 - loss: 0.0996
106/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9696 - loss: 0.0995
109/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9697 - loss: 0.0993
112/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9697 - loss: 0.0992
115/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9697 - loss: 0.0991
118/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9697 - loss: 0.0990
121/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9697 - loss: 0.0990
124/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9697 - loss: 0.0989
127/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9697 - loss: 0.0989
130/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9696 - loss: 0.0989
133/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9696 - loss: 0.0989
136/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9695 - loss: 0.0989
139/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9694 - loss: 0.0990
140/140 āāāāāāāāāāāāāāāāāāāā 3s 19ms/step - accuracy: 0.9694 - loss: 0.0990 - val_accuracy: 0.8302 - val_loss: 0.4380
Epoch 5/5
1/140 āāāāāāāāāāāāāāāāāāāā 3s 27ms/step - accuracy: 0.9375 - loss: 0.0922
4/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9655 - loss: 0.0828
7/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9728 - loss: 0.0743
10/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9751 - loss: 0.0707
13/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9760 - loss: 0.0694
16/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9774 - loss: 0.0670
19/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9783 - loss: 0.0661
22/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9791 - loss: 0.0655
25/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9798 - loss: 0.0648
28/140 āāāāāāāāāāāāāāāāāāāā 2s 18ms/step - accuracy: 0.9805 - loss: 0.0639
31/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9810 - loss: 0.0631
34/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9813 - loss: 0.0625
37/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9817 - loss: 0.0618
40/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9820 - loss: 0.0611
43/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9821 - loss: 0.0607
46/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9823 - loss: 0.0603
49/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9824 - loss: 0.0599
52/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9825 - loss: 0.0597
55/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9826 - loss: 0.0594
58/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9826 - loss: 0.0592
61/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9827 - loss: 0.0589
64/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9828 - loss: 0.0587
67/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9828 - loss: 0.0585
70/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9828 - loss: 0.0583
73/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9828 - loss: 0.0582
76/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9828 - loss: 0.0581
79/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9828 - loss: 0.0580
82/140 āāāāāāāāāāāāāāāāāāāā 1s 18ms/step - accuracy: 0.9828 - loss: 0.0578
85/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9828 - loss: 0.0577
88/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9827 - loss: 0.0576
91/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9827 - loss: 0.0575
94/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9827 - loss: 0.0574
97/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9827 - loss: 0.0573
100/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9827 - loss: 0.0572
103/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9826 - loss: 0.0571
106/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9826 - loss: 0.0571
109/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9825 - loss: 0.0571
112/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9825 - loss: 0.0570
115/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9825 - loss: 0.0570
118/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9824 - loss: 0.0570
121/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9824 - loss: 0.0569
124/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9823 - loss: 0.0569
127/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9823 - loss: 0.0569
130/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9822 - loss: 0.0569
133/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9822 - loss: 0.0569
136/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9821 - loss: 0.0569
139/140 āāāāāāāāāāāāāāāāāāāā 0s 18ms/step - accuracy: 0.9821 - loss: 0.0569
140/140 āāāāāāāāāāāāāāāāāāāā 3s 19ms/step - accuracy: 0.9821 - loss: 0.0568 - val_accuracy: 0.8342 - val_loss: 0.4805
1/47 āāāāāāāāāāāāāāāāāāāā 1s 35ms/step
14/47 āāāāāāāāāāāāāāāāāāāā 0s 4ms/step
26/47 āāāāāāāāāāāāāāāāāāāā 0s 4ms/step
39/47 āāāāāāāāāāāāāāāāāāāā 0s 4ms/step
47/47 āāāāāāāāāāāāāāāāāāāā 0s 5ms/step
47/47 āāāāāāāāāāāāāāāāāāāā 0s 5ms/step
Accuracy: 0.8342318059299192 Precision: 0.8191214470284238
Recall: 0.8567567567567568 Average accuracy: 0.8530260045137303 Average precision: 0.8497657590540433 Average recall: 0.8596650446760645
InĀ [32]:
from tensorflow.keras.layers import Dense, Embedding, Flatten
InĀ [33]:
accuracy = []
precision = []
recall = []
for dataset in chosen_datasets:
df = pd.read_csv(os.path.join("datasets", dataset))
X_train, X_test, y_train, y_test = train_test_split(df["text"].values, df["label"].values, test_size=0.25, random_state=1)
tokenizer = Tokenizer()
tokenizer.fit_on_texts(X_train)
sequences = tokenizer.texts_to_sequences(X_train)
max_length = max([len(seq) for seq in sequences])
padded_sequences = pad_sequences(sequences, maxlen=max_length)
new_sequences = tokenizer.texts_to_sequences(X_test)
new_padded_sequences = pad_sequences(new_sequences, maxlen=max_length)
model = Sequential()
model.add(Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=32))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
epochs = 3
history = model.fit(padded_sequences, y_train, validation_data=(new_padded_sequences, y_test), epochs=epochs)
print_metrics(history, epochs=epochs)
predictions = model.predict(new_padded_sequences)
correct = 0
predicted_labels = []
for i in range(len(predictions)):
prediction_label = 1 if predictions[i] > 0.5 else 0
predicted_labels.append(prediction_label)
if y_test[i] == prediction_label:
correct += 1
print_conf_matrix(y_test, predicted_labels)
accuracy.append(correct / len(predictions))
print(f"Accuracy: {correct / len(predictions)}")
print(f"Precision: {precision_score(y_test, predicted_labels)}")
precision.append(precision_score(y_test, predicted_labels))
print(f"Recall: {recall_score(y_test, predicted_labels)}")
recall.append(recall_score(y_test, predicted_labels))
print(f"Average accuracy: {np.average(accuracy)}")
print(f"Average precision: {np.average(precision)}")
print(f"Average recall: {np.average(recall)}")
Epoch 1/3
1/60 āāāāāāāāāāāāāāāāāāāā 24s 409ms/step - accuracy: 0.5000 - loss: 0.6940
13/60 āāāāāāāāāāāāāāāāāāāā 0s 4ms/step - accuracy: 0.4875 - loss: 0.7212
27/60 āāāāāāāāāāāāāāāāāāāā 0s 4ms/step - accuracy: 0.4957 - loss: 0.7182
42/60 āāāāāāāāāāāāāāāāāāāā 0s 4ms/step - accuracy: 0.4979 - loss: 0.7147
57/60 āāāāāāāāāāāāāāāāāāāā 0s 4ms/step - accuracy: 0.5032 - loss: 0.7116
60/60 āāāāāāāāāāāāāāāāāāāā 1s 5ms/step - accuracy: 0.5056 - loss: 0.7108 - val_accuracy: 0.7397 - val_loss: 0.6716
Epoch 2/3
1/60 āāāāāāāāāāāāāāāāāāāā 0s 15ms/step - accuracy: 0.7812 - loss: 0.6593
16/60 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.7134 - loss: 0.6588
31/60 āāāāāāāāāāāāāāāāāāāā 0s 4ms/step - accuracy: 0.7083 - loss: 0.6535
45/60 āāāāāāāāāāāāāāāāāāāā 0s 4ms/step - accuracy: 0.7047 - loss: 0.6473
60/60 āāāāāāāāāāāāāāāāāāāā 0s 4ms/step - accuracy: 0.7021 - loss: 0.6394 - val_accuracy: 0.8707 - val_loss: 0.4989
Epoch 3/3
1/60 āāāāāāāāāāāāāāāāāāāā 0s 14ms/step - accuracy: 0.9062 - loss: 0.4488
16/60 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.8733 - loss: 0.4560
31/60 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.8837 - loss: 0.4370
46/60 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.8902 - loss: 0.4218
60/60 āāāāāāāāāāāāāāāāāāāā 0s 4ms/step - accuracy: 0.8943 - loss: 0.4092 - val_accuracy: 0.8849 - val_loss: 0.3406
1/20 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step
20/20 āāāāāāāāāāāāāāāāāāāā 0s 2ms/step
Accuracy: 0.8848580441640379 Precision: 0.9140893470790378 Recall: 0.8471337579617835
Epoch 1/3
1/67 āāāāāāāāāāāāāāāāāāāā 26s 397ms/step - accuracy: 0.6875 - loss: 0.6822
16/67 āāāāāāāāāāāāāāāāāāāā 0s 4ms/step - accuracy: 0.5295 - loss: 0.7259
32/67 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.5201 - loss: 0.7188
47/67 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.5271 - loss: 0.7098
62/67 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.5392 - loss: 0.7004
67/67 āāāāāāāāāāāāāāāāāāāā 1s 5ms/step - accuracy: 0.5432 - loss: 0.6974 - val_accuracy: 0.6837 - val_loss: 0.5907
Epoch 2/3
1/67 āāāāāāāāāāāāāāāāāāāā 0s 14ms/step - accuracy: 0.6875 - loss: 0.6214
17/67 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.6959 - loss: 0.5842
33/67 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.7153 - loss: 0.5691
49/67 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.7192 - loss: 0.5633
65/67 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.7233 - loss: 0.5581
67/67 āāāāāāāāāāāāāāāāāāāā 0s 4ms/step - accuracy: 0.7242 - loss: 0.5570 - val_accuracy: 0.7943 - val_loss: 0.4604
Epoch 3/3
1/67 āāāāāāāāāāāāāāāāāāāā 0s 14ms/step - accuracy: 0.8438 - loss: 0.3803
17/67 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.8029 - loss: 0.4582
33/67 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.8150 - loss: 0.4446
49/67 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.8242 - loss: 0.4333
65/67 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.8302 - loss: 0.4244
67/67 āāāāāāāāāāāāāāāāāāāā 0s 4ms/step - accuracy: 0.8311 - loss: 0.4230 - val_accuracy: 0.8681 - val_loss: 0.3609
1/23 āāāāāāāāāāāāāāāāāāāā 0s 26ms/step
23/23 āāāāāāāāāāāāāāāāāāāā 0s 2ms/step
Accuracy: 0.8680851063829788 Precision: 0.8385416666666666 Recall: 0.9121813031161473
Epoch 1/3
1/53 āāāāāāāāāāāāāāāāāāāā 20s 388ms/step - accuracy: 0.5000 - loss: 0.6934
23/53 āāāāāāāāāāāāāāāāāāāā 0s 2ms/step - accuracy: 0.5144 - loss: 0.6989
48/53 āāāāāāāāāāāāāāāāāāāā 0s 2ms/step - accuracy: 0.5496 - loss: 0.6849
53/53 āāāāāāāāāāāāāāāāāāāā 1s 4ms/step - accuracy: 0.5564 - loss: 0.6810 - val_accuracy: 0.7102 - val_loss: 0.5711
Epoch 2/3
1/53 āāāāāāāāāāāāāāāāāāāā 0s 12ms/step - accuracy: 0.6875 - loss: 0.5906
27/53 āāāāāāāāāāāāāāāāāāāā 0s 2ms/step - accuracy: 0.7354 - loss: 0.5513
53/53 āāāāāāāāāāāāāāāāāāāā 0s 2ms/step - accuracy: 0.7427 - loss: 0.5366 - val_accuracy: 0.7424 - val_loss: 0.5081
Epoch 3/3
1/53 āāāāāāāāāāāāāāāāāāāā 0s 13ms/step - accuracy: 0.7500 - loss: 0.4979
27/53 āāāāāāāāāāāāāāāāāāāā 0s 2ms/step - accuracy: 0.8305 - loss: 0.4349
53/53 āāāāāāāāāāāāāāāāāāāā 0s 2ms/step - accuracy: 0.8386 - loss: 0.4227 - val_accuracy: 0.8157 - val_loss: 0.4428
1/18 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step
18/18 āāāāāāāāāāāāāāāāāāāā 0s 2ms/step
Accuracy: 0.815742397137746 Precision: 0.9572192513368984 Recall: 0.6532846715328468
Epoch 1/3
1/48 āāāāāāāāāāāāāāāāāāāā 18s 404ms/step - accuracy: 0.6250 - loss: 0.6904
17/48 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.5307 - loss: 0.7177
31/48 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.5209 - loss: 0.7102
47/48 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.5179 - loss: 0.7056
48/48 āāāāāāāāāāāāāāāāāāāā 1s 5ms/step - accuracy: 0.5176 - loss: 0.7052 - val_accuracy: 0.5168 - val_loss: 0.6840
Epoch 2/3
1/48 āāāāāāāāāāāāāāāāāāāā 0s 14ms/step - accuracy: 0.6562 - loss: 0.6651
17/48 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.6768 - loss: 0.6650
33/48 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.6965 - loss: 0.6636
48/48 āāāāāāāāāāāāāāāāāāāā 0s 4ms/step - accuracy: 0.7097 - loss: 0.6607 - val_accuracy: 0.6489 - val_loss: 0.6539
Epoch 3/3
1/48 āāāāāāāāāāāāāāāāāāāā 0s 14ms/step - accuracy: 0.8750 - loss: 0.5963
17/48 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.9203 - loss: 0.5991
33/48 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.9129 - loss: 0.5900
48/48 āāāāāāāāāāāāāāāāāāāā 0s 4ms/step - accuracy: 0.9045 - loss: 0.5844 - val_accuracy: 0.5878 - val_loss: 0.6301
1/16 āāāāāāāāāāāāāāāāāāāā 0s 25ms/step
16/16 āāāāāāāāāāāāāāāāāāāā 0s 2ms/step
Accuracy: 0.5877712031558185 Precision: 0.9491525423728814 Recall: 0.21374045801526717
Epoch 1/3
1/140 āāāāāāāāāāāāāāāāāāāā 1:24 605ms/step - accuracy: 0.3750 - loss: 0.7027
18/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.4551 - loss: 0.7263
34/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.4728 - loss: 0.7192
51/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.4815 - loss: 0.7147
68/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.4866 - loss: 0.7115
85/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.4910 - loss: 0.7090
102/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.4952 - loss: 0.7071
119/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.4991 - loss: 0.7054
137/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.5026 - loss: 0.7039
140/140 āāāāāāāāāāāāāāāāāāāā 1s 4ms/step - accuracy: 0.5033 - loss: 0.7035 - val_accuracy: 0.5350 - val_loss: 0.6635
Epoch 2/3
1/140 āāāāāāāāāāāāāāāāāāāā 2s 14ms/step - accuracy: 0.5938 - loss: 0.6386
18/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.6054 - loss: 0.6511
35/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.6262 - loss: 0.6419
52/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.6517 - loss: 0.6309
69/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.6719 - loss: 0.6206
86/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.6867 - loss: 0.6119
103/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.6994 - loss: 0.6031
120/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.7104 - loss: 0.5947
137/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.7198 - loss: 0.5871
140/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.7219 - loss: 0.5853 - val_accuracy: 0.7985 - val_loss: 0.4607
Epoch 3/3
1/140 āāāāāāāāāāāāāāāāāāāā 1s 14ms/step - accuracy: 0.7500 - loss: 0.4329
19/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.8746 - loss: 0.3650
36/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.8893 - loss: 0.3575
54/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.8961 - loss: 0.3538
71/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.8985 - loss: 0.3505
88/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.8997 - loss: 0.3477
105/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.9006 - loss: 0.3448
123/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.9014 - loss: 0.3415
140/140 āāāāāāāāāāāāāāāāāāāā 0s 3ms/step - accuracy: 0.9021 - loss: 0.3382 - val_accuracy: 0.8363 - val_loss: 0.3823
1/47 āāāāāāāāāāāāāāāāāāāā 1s 25ms/step
47/47 āāāāāāāāāāāāāāāāāāāā 0s 1ms/step
47/47 āāāāāāāāāāāāāāāāāāāā 0s 1ms/step
Accuracy: 0.8362533692722371 Precision: 0.8326639892904953 Recall: 0.8405405405405405 Average accuracy: 0.7985420240225636 Average precision: 0.898333359349196 Average recall: 0.693376146233317
InĀ [34]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_score, \
recall_score, confusion_matrix
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, WeightedRandomSampler
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import os
from tqdm.notebook import tqdm
from collections import defaultdict
from time import time
InĀ [35]:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
InĀ [36]:
class Bert(nn.Module):
def __init__(self, bert, bert_output_dim, output_dim):
super().__init__()
self.bert = bert
self.relu = nn.ReLU()
self.fc = nn.Linear(bert_output_dim, output_dim)
def forward(self, sent_id, mask):
logits = self.bert(sent_id, mask, return_dict=False)[0]
outputs = self.relu(logits)
outputs = self.fc(outputs)
return outputs
def plot_learning_curves(history):
sns.set_style(style='whitegrid')
fig = plt.figure(figsize=(20, 14))
plt.subplot(2, 2, 1)
plt.title('Accuracy', fontsize=15)
plt.plot(history['accuracy']['train'], label='Train')
plt.plot(history['accuracy']['val'], label='Test')
plt.xlabel('epoch', fontsize=15)
plt.legend()
plt.subplot(2, 2, 2)
plt.title('Loss', fontsize=15)
plt.plot(history['loss']['train'], label='Train')
plt.plot(history['loss']['val'], label='Test')
plt.xlabel('epoch', fontsize=15)
plt.legend()
plt.show()
def train(
model,
criterion,
optimizer,
train_loader,
test_loader,
num_epochs,
lr_scheduler,
device='cpu'
):
history = defaultdict(lambda: defaultdict(list))
for epoch in tqdm(range(num_epochs), total=num_epochs):
# Train
start_time = time()
model.train(True)
train_loss = 0
test_loss = 0
print(f'epoch: {epoch + 1}/{num_epochs}')
print('Train')
preds, labels = np.array([], dtype=int), np.array([], dtype=int)
for sent_id, mask, y_batch in train_loader:
sent_id, mask, y_batch = sent_id.to(device), mask.to(device), y_batch.to(device)
outputs = model(sent_id, mask).logits.squeeze()
if outputs.size() == torch.Size([]):
outputs = outputs.unsqueeze(0)
loss = criterion(outputs, y_batch.float())
loss.backward()
optimizer.step()
optimizer.zero_grad()
train_loss += np.sum(loss.detach().cpu().numpy())
y_pred = nn.Sigmoid()(outputs).detach().cpu().numpy() > 0.5
preds = np.concatenate((preds, y_pred))
labels = np.concatenate((labels, y_batch.detach().cpu().numpy()))
train_loss /= len(train_loader)
train_accuracy = accuracy_score(labels, preds)
train_precision = precision_score(labels, preds)
train_recall = recall_score(labels, preds)
history['loss']['train'].append(train_loss)
history['accuracy']['train'].append(train_accuracy)
history['precision']['train'].append(train_precision)
history['recall']['train'].append(train_recall)
lr_scheduler.step()
print('Test')
model.train(False)
preds, labels = np.array([], dtype=int), np.array([], dtype=int)
for sent_id, mask, y_batch in test_loader:
sent_id, mask, y_batch = sent_id.to(device), mask.to(device), y_batch.to(device)
with torch.no_grad():
outputs = model(sent_id, mask).logits.squeeze()
if outputs.size() == torch.Size([]):
outputs = outputs.unsqueeze(0)
loss = criterion(outputs, y_batch.float())
test_loss += np.sum(loss.detach().cpu().numpy())
y_pred = nn.Sigmoid()(outputs).detach().cpu().numpy() > 0.5
preds = np.concatenate((preds, y_pred))
labels = np.concatenate((labels, y_batch.detach().cpu().numpy()))
test_loss /= len(test_loader)
val_accuracy = accuracy_score(labels, preds)
val_precision = precision_score(labels, preds)
val_recall = recall_score(labels, preds)
history['loss']['val'].append(test_loss)
history['accuracy']['val'].append(val_accuracy)
history['precision']['val'].append(val_precision)
history['recall']['val'].append(val_recall)
print(f'Epoch {epoch + 1} of {num_epochs} took {time() - start_time:.3f}s')
print(f' training loss (in-iteration): \t{train_loss:.6f}')
print(f' validation loss (in-iteration): \t{test_loss:.6f}')
print(f'\n training accuracy: \t\t\t{train_accuracy * 100:.2f} %')
print(f' validation accuracy: \t\t\t{val_accuracy * 100:.2f} %')
print(f'\n training precision: \t\t\t{train_precision * 100:.2f} %')
print(f' validation precision: \t\t{val_precision * 100:.2f} %')
print(f'\n training recall: \t\t\t{train_recall * 100:.2f} %')
print(f' validation recall: \t\t\t{val_recall * 100:.2f} %')
if epoch > 0:
plot_learning_curves(history)
return model, history
def predict(model, test_batch_gen, device='cpu'):
res_outputs = np.array([])
res_targets = np.array([])
for batch in test_batch_gen:
input_ids, attention_mask, target = batch
input_ids, attention_mask, target = input_ids.to(device), \
attention_mask.to(device), \
target.to(device)
output = model(input_ids, attention_mask).logits.squeeze()
output = nn.Sigmoid()(output) > 0.5
res_outputs = np.append(res_outputs,
output.detach().cpu().numpy())
res_targets = np.append(res_targets,
target.view(-1, 1).cpu().numpy())
print(f'accuracy_score: {accuracy_score(res_targets, res_outputs)}')
print(f'precision_score: {precision_score(res_targets, res_outputs)}')
print(f'recall_score: {recall_score(res_targets, res_outputs)}')
print('confusion matrix')
print(print_conf_matrix(res_targets, res_outputs))
return accuracy_score(res_targets, res_outputs), precision_score(res_targets, res_outputs), recall_score(res_targets, res_outputs)
InĀ [37]:
accuracy = []
precision = []
recall = []
for dataset in chosen_datasets:
df = pd.read_csv(os.path.join("datasets", dataset))
tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased", use_fast=False)
bert = AutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased", output_attentions=False, output_hidden_states=False)
# model = Bert(bert, bert.classifier.out_features, 1)
bert.classifier = nn.Linear(in_features=768, out_features=1, bias=True)
model = bert
X_train, X_test, y_train, y_test = train_test_split(df["text"].values, df["label"].values, test_size=0.25, random_state=1)
max_seq_len = 256
tokens_train = tokenizer.batch_encode_plus(
X_train,
max_length = max_seq_len,
padding=True,
truncation=True
)
tokens_test = tokenizer.batch_encode_plus(
X_test,
max_length = max_seq_len,
padding=True,
truncation=True
)
train_seq = torch.tensor(tokens_train['input_ids'])
train_mask = torch.tensor(tokens_train['attention_mask'])
train_y = torch.tensor(y_train)
test_seq = torch.tensor(tokens_test['input_ids'])
test_mask = torch.tensor(tokens_test['attention_mask'])
test_y = torch.tensor(y_test)
batch_size = 16
train_data = TensorDataset(train_seq, train_mask, train_y)
train_labels, train_counts = np.unique(y_train, return_counts=True)
train_class_weights = [sum(train_counts) / c for c in train_counts]
train_weigths = [train_class_weights[e] for e in y_train]
train_sampler = WeightedRandomSampler(weights=train_weigths, num_samples=y_train.shape[0])
train_dataloader = DataLoader(train_data, batch_size=batch_size, sampler=train_sampler)
test_data = TensorDataset(test_seq, test_mask, test_y)
test_sampler = RandomSampler(test_data)
test_dataloader = DataLoader(test_data, batch_size=batch_size, sampler=test_sampler)
output_dim = 1
lr = 3e-5
num_epochs = 3
model = model.to(device)
optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=0.2)
criterion = nn.BCEWithLogitsLoss()
lr_scheduler = optim.lr_scheduler.ExponentialLR(optimizer=optimizer,
gamma=0.9, verbose=True)
model, history = train(
model,
criterion,
optimizer,
train_dataloader,
test_dataloader,
num_epochs,
lr_scheduler,
device
)
acc, prec, rec = predict(model, test_dataloader, device)
accuracy.append(acc)
precision.append(prec)
recall.append(rec)
Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert/distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/home/tech/.local/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:28: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.
warnings.warn("The verbose parameter is deprecated. Please use get_last_lr() "
epoch: 1/3 Train
Test
Epoch 1 of 3 took 37.691s training loss (in-iteration): 0.378731 validation loss (in-iteration): 0.255282 training accuracy: 80.95 % validation accuracy: 89.91 % training precision: 83.04 % validation precision: 89.31 % training recall: 79.86 % validation recall: 90.45 % epoch: 2/3 Train
Test
Epoch 2 of 3 took 37.818s training loss (in-iteration): 0.139737 validation loss (in-iteration): 0.312449 training accuracy: 95.53 % validation accuracy: 86.28 % training precision: 97.05 % validation precision: 80.93 % training recall: 93.87 % validation recall: 94.59 %
epoch: 3/3 Train
Test
Epoch 3 of 3 took 37.995s training loss (in-iteration): 0.066964 validation loss (in-iteration): 0.346790 training accuracy: 97.95 % validation accuracy: 88.96 % training precision: 98.33 % validation precision: 85.88 % training recall: 97.36 % validation recall: 92.99 %
accuracy_score: 0.889589905362776 precision_score: 0.8588235294117647 recall_score: 0.9299363057324841 confusion matrix
None
Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert/distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/home/tech/.local/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:28: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.
warnings.warn("The verbose parameter is deprecated. Please use get_last_lr() "
epoch: 1/3 Train
Test
Epoch 1 of 3 took 42.674s training loss (in-iteration): 0.378373 validation loss (in-iteration): 0.267634 training accuracy: 82.74 % validation accuracy: 89.08 % training precision: 86.11 % validation precision: 87.70 % training recall: 79.23 % validation recall: 90.93 % epoch: 2/3 Train
Test
Epoch 2 of 3 took 43.124s training loss (in-iteration): 0.150284 validation loss (in-iteration): 0.223829 training accuracy: 94.23 % validation accuracy: 91.49 % training precision: 95.00 % validation precision: 89.07 % training recall: 93.17 % validation recall: 94.62 %
epoch: 3/3 Train
Test
Epoch 3 of 3 took 43.237s training loss (in-iteration): 0.094092 validation loss (in-iteration): 0.235162 training accuracy: 96.45 % validation accuracy: 92.34 % training precision: 97.08 % validation precision: 89.03 % training recall: 95.68 % validation recall: 96.60 %
accuracy_score: 0.9234042553191489 precision_score: 0.8903394255874674 recall_score: 0.9660056657223796 confusion matrix
None
Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert/distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/home/tech/.local/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:28: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.
warnings.warn("The verbose parameter is deprecated. Please use get_last_lr() "
epoch: 1/3 Train
Test
Epoch 1 of 3 took 34.168s training loss (in-iteration): 0.452715 validation loss (in-iteration): 0.341314 training accuracy: 77.43 % validation accuracy: 84.26 % training precision: 75.58 % validation precision: 87.80 % training recall: 81.49 % validation recall: 78.83 % epoch: 2/3 Train
Test
Epoch 2 of 3 took 34.275s training loss (in-iteration): 0.195410 validation loss (in-iteration): 0.270207 training accuracy: 92.42 % validation accuracy: 89.62 % training precision: 93.94 % validation precision: 91.86 % training recall: 90.96 % validation recall: 86.50 %
epoch: 3/3 Train
Test
Epoch 3 of 3 took 34.272s training loss (in-iteration): 0.094524 validation loss (in-iteration): 0.277183 training accuracy: 97.01 % validation accuracy: 89.80 % training precision: 98.12 % validation precision: 93.93 % training recall: 95.72 % validation recall: 84.67 %
accuracy_score: 0.8980322003577818 precision_score: 0.9392712550607287 recall_score: 0.8467153284671532 confusion matrix
None
Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert/distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/home/tech/.local/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:28: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.
warnings.warn("The verbose parameter is deprecated. Please use get_last_lr() "
epoch: 1/3 Train
Test
Epoch 1 of 3 took 31.017s training loss (in-iteration): 0.548258 validation loss (in-iteration): 0.504199 training accuracy: 69.85 % validation accuracy: 76.73 % training precision: 77.69 % validation precision: 92.35 % training recall: 52.99 % validation recall: 59.92 % epoch: 2/3 Train
Test
Epoch 2 of 3 took 31.112s training loss (in-iteration): 0.238369 validation loss (in-iteration): 0.344670 training accuracy: 91.05 % validation accuracy: 85.60 % training precision: 90.49 % validation precision: 82.25 % training recall: 91.69 % validation recall: 91.98 %
epoch: 3/3 Train
Test
Epoch 3 of 3 took 31.097s training loss (in-iteration): 0.151144 validation loss (in-iteration): 0.423298 training accuracy: 94.73 % validation accuracy: 84.02 % training precision: 94.14 % validation precision: 82.67 % training recall: 96.01 % validation recall: 87.40 %
accuracy_score: 0.8402366863905325 precision_score: 0.8267148014440433 recall_score: 0.8740458015267175 confusion matrix
None
Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert/distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/home/tech/.local/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:28: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.
warnings.warn("The verbose parameter is deprecated. Please use get_last_lr() "
epoch: 1/3 Train
Test
Epoch 1 of 3 took 90.763s training loss (in-iteration): 0.473939 validation loss (in-iteration): 0.420007 training accuracy: 75.35 % validation accuracy: 81.13 % training precision: 76.22 % validation precision: 83.72 % training recall: 71.30 % validation recall: 77.16 % epoch: 2/3 Train
Test
Epoch 2 of 3 took 90.873s training loss (in-iteration): 0.225971 validation loss (in-iteration): 0.399078 training accuracy: 91.17 % validation accuracy: 83.29 % training precision: 91.58 % validation precision: 85.65 % training recall: 90.89 % validation recall: 79.86 %
epoch: 3/3 Train
Test
Epoch 3 of 3 took 90.666s training loss (in-iteration): 0.141609 validation loss (in-iteration): 0.475095 training accuracy: 95.08 % validation accuracy: 83.09 % training precision: 95.11 % validation precision: 79.00 % training recall: 95.07 % validation recall: 90.00 %
accuracy_score: 0.8308625336927223 precision_score: 0.7900355871886121 recall_score: 0.9 confusion matrix
None
InĀ [38]:
print(f"Average accuracy: {np.average(accuracy)}")
print(f"Average precision: {np.average(precision)}")
print(f"Average recall: {np.average(recall)}")
Average accuracy: 0.8764251162245923 Average precision: 0.8610369197385233 Average recall: 0.9033406202897469